Method and apparatus for remote detection and monitoring of functional chronotropic incompetence

- Corventis, Inc.

Methods and apparatus to determine the presence of and track functional chronotropic incompetence (hereinafter “CI”) in an in-home setting under conditions of daily living. The functional CI of the patient may be determined with one or more of a profile of measured patient heart rates, a measured maximum patient heart rate, or a peak of the heart rate profile. The functional CI of the patient may be determined with the measured heart rate profile, in which the measured heart rate profile may correspond to heart rates substantially less than the maximum heart rate of the patient, such that the heart rate can be safely measured when the patient is remote from a health care provider. The functional CI of the patient may be determined based a peak of the remotely measured heart rate profile, for example a peak corresponding to the mode of the heart rate distribution profile.

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Description
CROSS-REFERENCES TO RELATED APPLICATIONS

The present application is a non-provisional and claims to priority to the following provisional patent application: U.S. 61/253,866, filed on Oct. 22, 2009, entitled “Method and Apparatus for Remote Detection and Monitoring of Functional Chronotropic Incompetence”.

BACKGROUND OF THE INVENTION

Patients are often treated for diseases and/or conditions associated with a compromised status of the patient, for example a compromised physiologic status. In some instances, a patient may report symptoms that require diagnosis to determine the underlying cause. For example, a patient may report fainting or dizziness that requires diagnosis, in which long term monitoring of the patient can provide useful information as to the physiologic status of the patient. In some instances a patient may have suffered a heart attack and require care and/or monitoring after release from the hospital.

Chronotropic incompetence (hereinafter “CI”) can be a debilitating condition associated with high mortality and morbidity. Chronotropic incompetence can be defined as the inability for a patient to elevate heart rate to 85% of the age-predicted maximum heart rate (hereinafter “APMHR”) level during exercise in a clinical environment. The determination of the ability of the patient to raise HR can be done by subjecting a patient to exercise in a clinic to elevate the patient HR, for example with a treadmill in a clinic.

Work in relation to embodiments of the present invention suggests that known methods and apparatus for determining CI may be less than ideal. At least some of the known methods and apparatus test the patient in a clinical setting and may not determine the presence of CI when the patient is located remote from the clinic, for example located at home. Although successful in determining the presence of CI in a clinical setting, current methods that rely on a controlled environment such as a treadmill in a clinic may not be well suited to determine CI when the patient is located remote from the clinic. For example, in at least some instances the patient may be somewhat frail and not well suited to exercise on his or her own. Also, current methods of determining the maximum HR of the patient assume that the patient is able to exercise the level of his or her capacity when the maximum HR is measured, and in at least some instances such an assumption may not be appropriate, such as for patients with respiratory and cardiac diseases, as well as patients with physical disability.

Another approach to determining cardiac function related to CI in a patient can be to determine the heart rate reserve (hereinafter “HRR”) of the patient, in which the HRR is determined with the resting HR of the patient. However, in at least some instances it can be difficult to determine the resting HR of the patient in the clinic. In at least some instances, measurements of a patient in a clinic can be nervous and the heart rate can be elevated, for example with white coat syndrome, and the patient may receive an incorrect diagnosis in at least some instances. Further, at least some of the present methods of measuring HR remotely may not provide appropriate data to determine the resting HR when the patient is located remote from the clinic.

Therefore, a need exists for improved patient monitoring. Ideally, such improved patient monitoring would avoid at least some of the short-comings of the present methods and devices.

BRIEF SUMMARY OF THE INVENTION

Embodiments of the present invention provide methods and apparatus to determine the presence of and track functional CI in an in-home setting under conditions of daily living. The remote monitoring of the patient can determine the presence of functional CI and identify functional CI so as to allow appropriate intervention and treatment. The functional CI of the patient can be determined safely and in many ways with the patient located outside the clinic. For example, the functional CI of the patient may be determined with one or more of a profile of measured patient heart rates, a measured maximum patient heart rate, or a peak of the heart rate profile, such as the peak of a heart rate distribution profile. The functional CI of the patient may be determined with the measured heart rate profile, in which the measured heart rate profile may correspond to heart rates substantially less than the maximum heart rate of the patient, such that the heart rate can be safely measured when the patient is remote from a health care provider. Alternatively or in combination, the functional CI of the patient may be determined based on a peak of the remotely measured heart rate profile. Further, the functional CI may be determined based on statistical measurements of the heart rate profile such as a location, for example central tendency, and variability, for example dispersion, of the measured heart rate. For example, the relative amounts of the profile of heart rates above the peak and heart rates below the peak can be compared to determine the functional CI. The peak of the heart rate profile of the remote heart rate data may be used to determine the heart rate reserve and functional CI of the patient.

The measured distribution of heart rates of the remotely measured patient heart rate data can be combined with one or more of the measured activity data, measured respiration data, the measured orientation and the measured impedance data so as to determine the functional CI of the patient. The measured activity data of the patient can be combined with the heart rate data to determine a measured maximum heart rate of the patient when the patient exercises. For example, the peak activity of the patient can be determined and compared to a threshold value, and the maximum heart rate of the patient may correspond to the activity of the patient above the threshold. Alternatively or in combination, the maximum heart rate of the patient may comprise an estimated maximum heart rate of the patient, and the presence of functional CI determined based on the estimated maximum heart rate and the age predicted maximum heart rate, such that the functional CI may be determined without requiring elevation of the heart rate of the patient.

The measured patient data can come from one or more of many sources of data such as an adherent device, or an implantable device, or combinations thereof. An implantable device can be used to measure heart rate data. Alternatively or in combination an adherent device can be used to measure heart rate data. Additional data can be measured, for example accelerometer data from an adherent device.

In a first aspect, embodiments provide an apparatus to monitor a patient. A processor system comprises at least one processor having a tangible medium with instructions of a computer program embodied thereon, the processor system configured to receive heart rate data of the patient and determine a profile of the heart rates and wherein the processor is configured to identify chronotropic incompetence of the patient based on the profile of the heart rates.

In many embodiments, the computer program comprises instructions to identify the functional CI with one or more measurement of location of the heart rate data, measures of dispersion and variability of the heart rate data, skewness and kurtosis of the heart rate data, or comparison of portions around a mode of a single modal mounded distribution.

In many embodiments, the computer program comprises instructions to determine a peak of the profile and a first portion of the profile and a second portion of the profile, the first portion corresponding to a first amount of occurrences of first heart rates less than the peak and the second portion corresponding to a second amount of occurrences of second heart rates greater than the peak and wherein the chronotropic incompetence is identified based on the second amount smaller than the first amount.

In another aspect, embodiments provide an apparatus to monitor a remote patient, the apparatus comprises a processor system comprising at least one processor having a tangible medium with instructions of a computer program embodied thereon. The processor system is configured to receive heart rate data of the remote patient and determine a distribution of the heart rates, and the processor is configured to identify a chronotropic incompetence of the patient based on the distribution of heart rates.

In many embodiments, the computer program comprises instructions to receive respiration data of the patient and activity data of the patient and instructions to combine the heart rate data with the respiration data and activity data to identify the chronotropic incompetence.

In many embodiments, the computer program comprises instructions to determine a peak of the distribution and a first portion of the distribution and a second portion of the distribution, the first portion corresponding to a first amount of occurrences of first heart rates less than the peak and the second portion of the distribution corresponding to a second amount of occurrences of second heart rates greater than the peak. The chronotropic incompetence is identified based on the second amount smaller than the first amount.

In another aspect, embodiments provide a method of monitoring a patient. A processor system is provided which comprises at least one processor having a tangible medium with instructions of a computer program embodied thereon, the processor system configured to receive heart rate data of the patient and determine a profile of the heart rates. The chronotropic incompetence of the patient is identified based on the profile of the heart rates.

In another aspect, embodiments provide an apparatus to monitor a remote patient. A processor system comprises at least one processor having a tangible medium with instructions of a computer program embodied thereon, the processor system configured to receive data of the remote patient comprising heart rate data of the patient and activity data of the patient. The processor system comprises instructions to determine activity of the patient to a threshold activity amount, and the processor system comprises instructions to identify a chronotropic incompetence of the patient based on the heart rate data corresponding to activity of the patient above the threshold.

In many embodiments, the processor system comprises instructions to determine a maximum heart rate of the heart rate data corresponding to the activity of the patient above the threshold.

In many embodiments, the processor system comprises instructions to determine a correlation of the maximum heart rate with one or more of the patient activity, patient body posture, patient breath rate or patient respiration rate and wherein the processor system is configured to identify CI based on the correlation.

In many embodiments, the data of the patient comprises drug data of the patient and wherein the processor system comprises instructions to identify CI based on the drug data and the correlation.

In many embodiments, the patient data comprises data from an adherent device measured remotely and wherein the processor system comprises instructions to determine the threshold amount from a plurality of remote patients and corresponds to a percentile of patient activity of the plurality of remote patients.

In another aspect, embodiments provide a method of monitoring a remote patient. A processor system is provided that comprises at least one processor having a tangible medium with instructions of a computer program embodied thereon, and the processor system is configured to receive data of the remote patient comprising heart rate data of the patient and activity data of the patient. The processor system comprises instructions to determine activity of the patient to a threshold activity amount. A chronotropic incompetence of the patient is identified based on the heart rate data corresponding to activity of the patient above the threshold.

In another aspect, embodiments provide an apparatus to monitor a remote patient. A processor system comprises at least one processor having a tangible medium with instructions of a computer program embodied thereon, and the processor system comprises instructions to receive heart rate data of the remote patient and to determine a peak of heart rates of the remote patient. The processor system comprises instructions to identify a chronotropic incompetence of the patient based on the peak.

In many embodiments, the heart rates comprise a profile of heart rates, and the peak comprises a peak of the profile.

In many embodiments, the heart rates comprise a distribution of heart rates, and the peak comprises a mode of the distribution.

In many embodiments, the processor system comprises instructions to determine a heart rate reserve based on a difference of a maximum age predicted maximum heart rate and the peak, and the processor system is configured to determine the CI based on the heart rate reserve determined with the peak.

In another aspect, embodiments provide a method of monitoring a remote patient. A processor system is provided that comprises at least one processor having a tangible medium with instructions of a computer program embodied thereon, and the processor system comprises instructions to receive heart rate data of the remote patient and to determine a peak of heart rates of the remote patient. A chronotropic incompetence of the patient is identified based on the peak.

In another aspect, embodiments provide an apparatus to monitor a patient having a skin. An adherent device to measure patient data comprises wireless communication circuitry and measurement circuitry, the measurement circuitry is coupled to at least two electrodes, a respiration sensor and an activity sensor. The adherent device comprising a support with an adhesive to adhere the at least two electrodes to the skin and support the wireless communication circuitry, the processor circuitry and the measurement circuitry with the skin. A server is located remote from the patient to receive the patient data. A gateway is coupled to each of the adherent device and the server with wireless communication to transmit the patient data. One or more of the adherent device, the server or the gateway comprises at least one processor having a tangible memory medium with instructions of a computer program embodied thereon to determine a chronotropic incompetence of the patient based on the patient data measured with the at least two electrodes, the respiration sensor and the activity sensor.

In many embodiments, the at least one processor comprises instructions to determine a distribution of heart rates of the patient and wherein the at least one processor is configured to determine the chronotropic incompetence based on the distribution heart rates.

In many embodiments, the distribution of heart rates of the patient corresponds to a plurality of heart levels and an occurrence of each level.

In many embodiments, the computer program comprises instructions to determine a peak of the distribution and a first portion of the distribution and a second portion of the distribution, the first portion corresponding to a first amount of occurrences of first heart rates less than the peak and the second portion of the distribution corresponding to a second amount of occurrences of second heart rates greater than the peak and wherein the chronotropic incompetence is determined based on the second amount smaller than the first amount.

In many embodiments, the at least one processor comprises instructions to fit the distribution to a Gaussian distribution and determine a skew of the distribution and wherein the chronotropic incompetence is determined based on the skew.

In many embodiments, the at least one processor comprises instructions to determine a distribution of heart rates of the patient, the distribution corresponding heart rates less than a maximum heart rate of the patient and wherein the at least one processor is configured to determine the chronotropic incompetence based on the distribution heart rate intervals corresponding to less than the maximum heart rate of the patient.

In many embodiments, the at least one processor comprises instructions to determine a distribution of heart rates of the patient, the distribution corresponding to heart rates less than a maximum heart rate of the patient and wherein the at least one processor comprises instructions to determine the maximum heart rate of the patient based on the distribution heart rate intervals corresponding to less than the maximum heart rate of the patient.

In many embodiments, the at least one processor comprises instructions to determine the chronotropic incompetence of the patient based on the maximum heart rate of the patient.

In many embodiments, the at least one processor comprises instructions to determine the maximum heart rate of the patient based on the distribution of heart rates corresponding to less than the maximum heart rate of the patient.

In another aspect, embodiments provide a method of monitoring a patient. Heart rate data of the patient is measured. A processor system is provided which comprises at least one processor having a tangible medium with instructions of a computer program embodied thereon. The processor system receives heart rate data of the patient and determines a distribution of the heart rates, and the processor determines a chronotropic incompetence of the patient based on the distribution of heart rates.

In many embodiments, the heart rate data comprise data measured from a patch adhered to the patient for at least about one week, and the heart rate data is transmitted with wireless communication.

In another aspect, embodiments provide an apparatus to monitor a patient. The apparatus comprises an adherent device means for measuring patient data, and a processor means for determining a chronotropic incompetence of the patient. The adherent device means may comprise the adherent device as described herein and the processor means for determining the chronotropic incompetence of the patient may comprise the computer readable instructions embedded on one or more processor as described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows a patient and a monitoring system comprising an adherent device, according to embodiments of the present invention;

FIG. 1B shows a bottom view of the adherent device as in FIG. 1A comprising an adherent patch;

FIG. 1C shows a top view of the adherent patch, as in FIG. 1B;

FIG. 1D shows a printed circuit board and electronic components over the adherent patch, as in FIG. 1C;

FIG. 1D1 shows an equivalent circuit that can be used to determine optimal frequencies for determining patient hydration, according to embodiments of the present invention;

FIG. 1D2 shows adherent devices as in FIGS. 1A-1D positioned on a patient to determine orientation of the adherent patch on the patient, according to embodiments of the present invention;

FIG. 1D3 shows vectors from a 3D accelerometer to determine orientation of the measurement axis of the patch adhered on the patient, according to embodiments of the present invention;

FIG. 1E shows batteries positioned over the printed circuit board and electronic components as in FIG. 1D;

FIG. 1F shows a top view of an electronics housing and a breathable cover over the batteries, electronic components and printed circuit board as in FIG. 1E;

FIG. 1G shows a side view of the adherent device as in FIGS. 1A to 1F;

FIG. 1H shown a bottom isometric view of the adherent device as in FIGS. 1A to 1G;

FIGS. 1I and 1J show a side cross-sectional view and an exploded view, respectively, of the adherent device as in FIGS. 1A to 1H;

FIGS. 1I1 and 1J1 show a side cross-sectional view and an exploded view, respectively, of embodiments of the adherent device with a temperature sensor affixed to the gel cover;

FIG. 1K shows at least one electrode configured to electrically couple to a skin of the patient through a breathable tape, according to embodiments of the present invention;

FIG. 2 shows a method of monitoring a person, in accordance with embodiments of the present invention;

FIGS. 3A1 to 3A5 show heart rate, activity index, body posture, impedance, and respiration rate measured from an adherent device adhered to the skin of the patient;

FIG. 3B shows measured patient heart rate profile data in accordance with embodiments of the present invention;

FIG. 3C shows average maximum activity of patients based on age for ages from about 20 to about 90;

FIG. 3D1 shows correlation of heart rate with activity for patients without functional CI; and

FIG. 3D2 shows correlation of heart rate with activity for patients with functional CI.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments comprise an adherent wireless communication apparatus and methods to measure patient data and determine the presence of functional chronotropic incompetence (CI). The patient measurement device may comprise one or more of an adherent device or an implantable device, and processor system can determine CI with heart rate and activity data collected from the patient under conditions of daily living, for example when the patient is home.

As used herein, chronotropic incompetence encompasses a failure of the heart rate to elevate sufficiently when the patient is active. For example, although CI may comprise a failure of the heart rate to elevate to a percentage amount of 85% of the age predicted maximum heart rate during exercise, this amount can change based on pharmacological modification of heart rate response. Therefore, the determination of the CI of the patient can change based on treatment of the patient with pharmacologic compositions, and the determination of the CI of the patient can be based on patient treatment with medication in additional to measure physiological patient data as described herein.

As used herein functional CI encompasses a CI condition where the patient's heart rate fails to accommodate the patient's activities of daily living, resulting in debilitation under sub-maximum activity levels and heart rates.

The adherent device and processor system are capable of monitoring and tracking patient activity and heart rate (hereinafter “HR”) so as to assess CI in a natural living environment outside the clinic, such as at home. The adherent device can also measure and compute respiratory rate and patient activity, such that correspondence among CI, respiration and activity can be determined. For example one or more processors may comprise instructions of a computer program so as to correlate the impact of CI to changes in other physiological parameters and patient symptoms. This combination of patient data can improve determination of the CI and correlate the CI to patient symptoms. For example the level of debilitation that CI is causing can be correlated to patient symptoms.

The adherent device and processor system can measure patient heart rate data and determine a maximum heart rate of the patient that can be used to monitor the patient. For example, the maximum heart rate of the patient may be determined without a cardiac stress test, and with patient heart rate data that is less than the actual maximum heart rate of the patient, such that the maximum heart rate can be determined safely when the patient is remote from a clinic. For example, an estimated maximum heart rate of the patient can be determined based on a patient a histogram distribution of the heart rate. Alternatively or in combination, the measured heart rate data can be adjusted based on one or more of patient activity and patient respiration.

The determined maximum heart rate of the patient can be combined in many ways with patient data to monitor the patient and trigger alerts when the patient is at risk, for example. The adherent device and processor system can determine the age predicted maximum heart rate, the age predicted heart rate reserve, the percent heart rate reserve. For example the adherent device and processor system can be configured to determine the age predicted maximum heart rate (APMHR) based on the patient age (hereinafter “AGE”) with the formula:
APMHR=220−AGE.

The APMHR can be combined with the determined maximum heart rate of the patient to determine the CI of the patient. For example, the ratio of the maximum heart rate to the APMHR and corresponding percentage can be determined. When the maximum HR of the patient corresponds to less than about 85% of the APMHR, the patient be identified as having CI.

The adherent device and processor system can determine the age predicted heart rate reserve (hereinafter “APHRR”) with the formula
APHRR=APMHR−resting HR.

The adherent device and processor system can determine the percent heart rate reserve (hereinafter “% HRR”) with the formula
% HRR=[(maximum HR)−(Resting HR)]*100/APHRR.

The adherent device and processor system can determine histograms for each of the HR, the RR interval and the activity, and determine the correlation between these measurement data and derive indices from each of these measurement data.

In the many embodiments, the adherent device can communicate wirelessly so as to transmit the multi-sensor data to a server located remote from the patient. The adherent device can communicate to the server with a wireless communication gateway. The wireless communication gateway can receive data from the adherent device with wireless communication, for example Bluetooth™, and the gateway can transmit the data to the server with wireless communication, for example a cellular communication protocol.

The remote server may comprise a computer program having instructions embodied in a tangible memory medium so as to instruct the processor to combine the collected data from the device as well as demographic and medication information resident on the server, in order to determine the presence of patient CI. The instructions of the program can also calculate the CI parameters and raise an alert if a adverse condition is detected. Alternatively or in combination, the gateway near the patient may comprise a processor having a tangible memory medium, and the gateway may comprise instructions of a computer program embodied on the tangible medium, so as to instruct the gateway processor to combine the collected data from the adherent device as well as the demographic and the medication information.

In many embodiments, the adherent device may comprise a processor and perform real-time diagnostic assessment of CI and alert the patient and/or care provider via audio and/or visual cues based on standard CI classification cut-off levels. This is possible with an adherent device that can store the theoretical age predicted maximum heart rate (hereinafter “APMHR”) and then track patient activity and heart rate so as to assess CI in real time. Alternatively or in combination, the adherent device can retrieve patient data related to the APMHR from the server, for example the patient's age when the patient has used the adherent device before and the patient data is stored on a database of the server. This retrieval of the APMHR data can improve the accuracy of the device as used and prevent errors, as the patient age, for example, can be stored in the data base such that the physician or patient from entering the age manually and may also avoid data entry errors.

Alternatively or in combination, the adherent device may store the CI assessment data for future offline data download, or transmit the data in real-time directly or indirectly (through an intermediary device that is paired to the adherent device) to a data storage entity.

The systems, methods and apparatus as described herein may comprise instructions of a processor system so as to determine functional CI based on an analysis of the envelop of HR histogram profile and the profile of HR change with activity so as to assess cardio-acceleration and cardio-blunting.

There may be additional embodiments and implementations for this method and apparatus based on the teachings described herein that will be apparent to a person of ordinary skill in the art.

FIG. 1A shows a patient P and a monitoring system 10. Patient P comprises a midline M, a first side S1, for example a right side, and a second side S2, for example a left side. Monitoring system 10 comprises an adherent device 100. Adherent device 100 can be adhered to a patient P at many locations, for example thorax T of patient P. In many embodiments, the adherent device may adhere to one side of the patient, from which side data can be collected. Work in relation with embodiments of the present invention suggests that location on a side of the patient can provide comfort for the patient while the device is adhered to the patient. The monitoring system 10 and adherent device 100 may comprise components as described in U.S. Pub. No. US-2009-0076345-A1, entitled “Adherent Device with Multiple Physiological Sensors”, the full disclosure of which is incorporated herein by reference and suitable for combination in accordance with some embodiments of the present invention as described herein.

Monitoring system 10 includes components to transmit data to a remote center 106. Remote center 106 can be located in a different building from the patient, for example in the same town as the patient, and can be located as far from the patient as a separate continent from the patient, for example the patient located on a first continent and the remote center located on a second continent. Adherent device 100 can communicate wirelessly to an intermediate device 102, for example with a single wireless hop from the adherent device on the patient to the intermediate device. Intermediate device 102 can communicate with remote center 106 in many ways, for example with an Internet connection and/or with a cellular connection. In many embodiments, monitoring system 10 comprises a distributed processor system with at least one processor comprising a tangible medium of device 100, at least one processor 102P of intermediate device 102, and at least one processor 106P at remote center 106, each of which processors can be in electronic communication with the other processors. At least one processor 102P comprises a tangible medium 102T, and at least one processor 106P comprises a tangible medium 106T. Remote processor 106P may comprise a backend server located at the remote center. Remote center 106 can be in communication with a health care provider 108A with a communication system 107A, such as the Internet, an intranet, phone lines, wireless and/or satellite phone. Health care provider 108A, for example a family member, can be in communication with patient P with a communication, for example with a two way communication system, as indicated by arrow 109A, for example by cell phone, email, or landline. Remote center 106 can be in communication with a health care professional, for example a physician 108B, with a communication system 107B, such as the Internet, an intranet, phone lines, wireless and/or satellite phone. Physician 108B can be in communication with patient P with a communication, for example with a two way communication system, as indicated by arrow 109B, for example by cell phone, email, landline. Remote center 106 can be in communication with an emergency responder 108C, for example a 911 operator and/or paramedic, with a communication system 107C, such as the Internet, an intranet, phone lines, wireless and/or satellite phone. Emergency responder 108C can travel to the patient as indicated by arrow 109C. Thus, in many embodiments, monitoring system 10 comprises a closed loop system in which patient care can be monitored and implemented from the remote center in response to signals from the adherent device.

In many embodiments, the adherent device may continuously monitor physiological parameters, communicate wirelessly with a remote center, and provide alerts when necessary. The system may comprise an adherent patch, which attaches to the patient's thorax and contains sensing electrodes, battery, memory, logic, and wireless communication capabilities. In some embodiments, the patch can communicate with the remote center, via the intermediate device in the patient's home. In some embodiments, remote center 106 receives the patient data and applies a patient evaluation algorithm, for example the prediction algorithm to predict patient physiological or mental deterioration. In some embodiments, the algorithm may comprise an algorithm to predict impending patient physiological or mental deterioration, for example based on decreased hydration and activity. When a flag is raised, the center may communicate with the patient, hospital, nurse, and/or physician to allow for therapeutic intervention, for example to prevent further physiological or mental deterioration.

The adherent device may be affixed and/or adhered to the body in many ways. For example, with at least one of the following an adhesive tape, a constant-force spring, suspenders around shoulders, a screw-in microneedle electrode, a pre-shaped electronics module to shape fabric to a thorax, a pinch onto roll of skin, or transcutaneous anchoring. Patch and/or device replacement may occur with a keyed patch (e.g. two-part patch), an outline or anatomical mark, a low-adhesive guide (place guide|remove old patch|place new patch|remove guide), or a keyed attachment for chatter reduction. The patch and/or device may comprise an adhesiveless embodiment (e.g. chest strap), and/or a low-irritation adhesive for sensitive skin. The adherent patch and/or device can comprise many shapes, for example at least one of a dogbone, an hourglass, an oblong, a circular or an oval shape.

In many embodiments, the adherent device may comprise a reusable electronics module with replaceable patches, and each of the replaceable patches may include a battery. The module may collect cumulative data for approximately 90 days and/or the entire adherent component (electronics+patch) may be disposable. In a completely disposable embodiment, a “baton” mechanism may be used for data transfer and retention, for example baton transfer may include baseline information. In some embodiments, the device may have a rechargeable module, and may use dual battery and/or electronics modules, wherein one module 101A can be recharged using a charging station 103 while the other module 101B is placed on the adherent patch with connectors. In some embodiments, the intermediate device 102 may comprise the charging module, data transfer, storage and/or transmission, such that one of the electronics modules can be placed in the intermediate device for charging and/or data transfer while the other electronics module is worn by the patient.

System 10 can perform the following functions: initiation, programming, measuring, storing, analyzing, communicating, predicting, and displaying. The adherent device may contain a subset of the following physiological sensors: bioimpedance, respiration, respiration rate variability, heart rate (ave, min, max), heart rhythm, heart rate variability (HRV), heart rate turbulence (HRT), heart sounds (e.g. S3), respiratory sounds, blood pressure, activity, posture, wake/sleep, orthopnea, temperature/heat flux, and weight. The activity sensor may comprise one or more of the following: ball switch, accelerometer, minute ventilation, HR, bioimpedance noise, skin temperature/heat flux, BP, muscle noise, posture.

The adherent device can wirelessly communicate with remote center 106. The communication may occur directly (via a cellular or Wi-Fi network), or indirectly through intermediate device 102. Intermediate device 102 may consist of multiple devices, which can communicate wired or wirelessly to relay data to remote center 106.

In many embodiments, instructions are transmitted from remote site 106 to a processor supported with the adherent patch on the patient, and the processor supported with the patient can receive updated instructions for the patient treatment and/or monitoring, for example while worn by the patient.

FIG. 1B shows a bottom view of adherent device 100 as in FIG. 1A comprising an adherent patch 110. Adherent patch 110 comprises a first side, or a lower side 110A, that is oriented toward the skin of the patient when placed on the patient. In many embodiments, adherent patch 110 comprises a tape 110T which is a material, preferably breathable, with an adhesive 116A. Patient side 110A comprises adhesive 116A to adhere the patch 110 and adherent device 100 to patient P. Electrodes 112A, 112B, 112C and 112D are affixed to adherent patch 110. In many embodiments, at least four electrodes are attached to the patch, for example six electrodes. In some embodiments the patch comprises two electrodes, for example two electrodes to measure the electrocardiogram (ECG) of the patient. Gel 114A, gel 114B, gel 114C and gel 114D can each be positioned over electrodes 112A, 112B, 112C and 112D, respectively, to provide electrical conductivity between the electrodes and the skin of the patient. In many embodiments, the electrodes can be affixed to the patch 110, for example with known methods and structures such as rivets, adhesive, stitches, etc. In many embodiments, patch 110 comprises a breathable material to permit air and/or vapor to flow to and from the surface of the skin.

FIG. 1C shows a top view of the adherent patch 100, as in FIG. 1B. Adherent patch 100 comprises a second side, or upper side 110B. In many embodiments, electrodes 112A, 112B, 112C and 112D extend from lower side 110A through adherent patch 110 to upper side 110B. An adhesive 116B can be applied to upper side 110B to adhere structures, for example a breathable cover, to the patch such that the patch can support the electronics and other structures when the patch is adhered to the patient. The PCB may comprise completely flex PCB, rigid PCB, rigid PCB combined flex PCB and/or rigid PCB boards connected by cable.

FIG. 1D shows a printed circuit boards and electronic components over adherent patch 110, as in FIGS. 1A to 1C. In some embodiments, a printed circuit board (PCB), for example flex printed circuit board 120, may be connected to electrodes 112A, 112B, 112C and 112D with connectors 122A, 122B, 122C and 122D. Flex printed circuit board 120 can include traces 123A, 123B, 123C and 123D that extend to connectors 122A, 122B, 122C and 122D, respectively, on the flex PCB. Connectors 122A, 122B, 122C and 122D can be positioned on flex printed circuit board 120 in alignment with electrodes 112A, 112B, 112C and 112D so as to electrically couple the flex PCB with the electrodes. In some embodiments, connectors 122A, 122B, 122C and 122D may comprise insulated wires and/or a film with conductive ink that provide strain relief between the PCB and the electrodes. For example, connectors 122A, 122B, 122C and 122D may comprise a flexible film, such as at least one of known polyester film or known polyurethane file coated with a conductive ink, for example a conductive silver ink. Examples of structures to provide strain relief are also described in U.S. patent application Ser. No. 12/209,288, entitled “Adherent Device with Multiple Physiological Sensors”, filed on Sep. 12, 2008. In some embodiments, additional PCB's, for example rigid PCB's 120A, 120B, 120C and 120D, can be connected to flex printed circuit board 120. Electronic components 130 can be connected to flex printed circuit board 120 and/or mounted thereon. In some embodiments, electronic components 130 can be mounted on the additional PCB's.

Electronic components 130 comprise components to take physiologic measurements, transmit data to remote center 106 and receive commands from remote center 106. In many embodiments, electronics components 130 may comprise known low power circuitry, for example complementary metal oxide semiconductor (CMOS) circuitry components. Electronics components 130 comprise an activity sensor and activity circuitry 134, impedance circuitry 136 and electrocardiogram circuitry, for example ECG circuitry 136. In some embodiments, electronics circuitry 130 may comprise a microphone and microphone circuitry 142 to detect an audio signal from within the patient, and the audio signal may comprise a heart sound and/or a respiratory sound, for example an S3 heart sound and a respiratory sound with rales and/or crackles.

Electronics circuitry 130 may comprise a temperature sensor, for example a thermistor in contact with the skin of the patient, and temperature sensor circuitry 144 to measure a temperature of the patient, for example a temperature of the skin of the patient. A temperature sensor may be used to determine the sleep and wake state of the patient. The temperature of the patient can decrease as the patient goes to sleep and increase when the patient wakes up.

Work in relation to embodiments of the present invention suggests that skin temperature may effect impedance and/or hydration measurements, and that skin temperature measurements may be used to correct impedance and/or hydration measurements. In some embodiments, increase in skin temperature or heat flux can be associated with increased vaso-dilation near the skin surface, such that measured impedance measurement decreased, even through the hydration of the patient in deeper tissues under the skin remains substantially unchanged. Thus, use of the temperature sensor can allow for correction of the hydration signals to more accurately assess the hydration, for example extra cellular hydration, of deeper tissues of the patient, for example deeper tissues in the thorax.

Electronics circuitry 130 may comprise a processor 146. Processor 146 comprises a tangible medium, for example read only memory (ROM), electrically erasable programmable read only memory (EEPROM) and/or random access memory (RAM). Electronic circuitry 130 may comprise real time clock and frequency generator circuitry 148. In some embodiments, processor 136 may comprise the frequency generator and real time clock. The processor can be configured to control a collection and transmission of data from the impedance circuitry electrocardiogram circuitry and the accelerometer. In many embodiments, device 100 comprises a distributed processor system, for example with multiple processors on device 100.

In many embodiments, electronics components 130 comprise wireless communications circuitry 132 to communicate with remote center 106. Printed circuit board 120 may comprise an antenna to facilitate wireless communication. The antenna may be integral with printed circuit board 120 or may be separately coupled thereto. The wireless communication circuitry can be coupled to the impedance circuitry, the electrocardiogram circuitry and the accelerometer to transmit to a remote center with a communication protocol at least one of the hydration signal, the electrocardiogram signal or the inclination signal. In specific embodiments, wireless communication circuitry is configured to transmit the hydration signal, the electrocardiogram signal and the inclination signal to the remote center with a single wireless hop, for example from wireless communication circuitry 132 to intermediate device 102. The communication protocol comprises at least one of Bluetooth, ZigBee, WiFi, WiMAX, IR, amplitude modulation or frequency modulation. In many embodiments, the communications protocol comprises a two way protocol such that the remote center is capable of issuing commands to control data collection.

Intermediate device 102 may comprise a data collection system to collect and store data from the wireless transmitter. The data collection system can be configured to communicate periodically with the remote center. The data collection system can transmit data in response to commands from remote center 106 and/or in response to commands from the adherent device.

Activity sensor and activity circuitry 134 can comprise many known activity sensors and circuitry. In many embodiments, the accelerometer comprises at least one of a piezoelectric accelerometer, capacitive accelerometer or electromechanical accelerometer. The accelerometer may comprises a 3-axis accelerometer to measure at least one of an inclination, a position, an orientation or acceleration of the patient in three dimensions. Work in relation to embodiments of the present invention suggests that three dimensional orientation of the patient and associated positions, for example sitting, standing, lying down, can be very useful when combined with data from other sensors, for example ECG data and/or hydration data.

Impedance circuitry 136 can generate both hydration data and respiration data. In many embodiments, impedance circuitry 136 is electrically connected to electrodes 112A, 112B, 112C and 112D in a four pole configuration, such that electrodes 112A and 112D comprise outer electrodes that are driven with a current and comprise force electrodes that force the current through the tissue. The current delivered between electrodes 112A and 112D generates a measurable voltage between electrodes 112B and 112C, such that electrodes 112B and 112C comprise inner, sense, electrodes that sense and/or measure the voltage in response to the current from the force electrodes. In some embodiments, electrodes 112B and 112C may comprise force electrodes and electrodes 112A and 112D may comprise sense electrodes. The voltage measured by the sense electrodes can be used to measure the impedance of the patient and determine the respiration rate and/or hydration of the patient. The electrocardiogram circuitry may be coupled to the sense electrodes to measure the electrocardiogram signal, for example as described in U.S. patent application Ser. No. 12/209,288, entitled “Adherent Device with Multiple Physiological Sensors”, filed on Sep. 12, 2008.

FIG. 1D1 shows an equivalent circuit 152 that can be used to determine optimal frequencies for measuring patient hydration. Work in relation to embodiments of the present invention indicates that the frequency of the current and/or voltage at the force electrodes can be selected so as to provide impedance signals related to the extracellular and/or intracellular hydration of the patient tissue. Equivalent circuit 152 comprises an intracellular resistance 156, or R(ICW) in series with a capacitor 154, and an extracellular resistance 158, or R(ECW). Extracellular resistance 158 is in parallel with intracellular resistance 156 and capacitor 154 related to capacitance of cell membranes. In many embodiments, impedances can be measured and provide useful information over a wide range of frequencies, for example from about 0.5 kHz to about 200 KHz. Work in relation to embodiments of the present invention suggests that extracellular resistance 158 can be significantly related extracellular fluid and to patient physiological or mental physiological or mental deterioration, and that extracellular resistance 158 and extracellular fluid can be effectively measured with frequencies in a range from about 0.5 kHz to about 20 kHz, for example from about 1 kHz to about 10 kHz. In some embodiments, a single frequency can be used to determine the extracellular resistance and/or fluid. As sample frequencies increase from about 10 kHz to about 20 kHz, capacitance related to cell membranes decrease the impedance, such that the intracellular fluid contributes to the impedance and/or hydration measurements. Thus, many embodiments of the present invention measure hydration with frequencies from about 0.5 kHz to about 20 kHz to determine patient hydration.

In many embodiments, impedance circuitry 136 can be configured to determine respiration of the patient. In specific embodiments, the impedance circuitry can measure the hydration at 25 Hz intervals, for example at 25 Hz intervals using impedance measurements with a frequency from about 0.5 kHz to about 20 kHz.

ECG circuitry 138 can generate electrocardiogram signals and data from two or more of electrodes 112A, 112B, 112C and 112D in many ways. In some embodiments, ECG circuitry 138 is connected to inner electrodes 112B and 122C, which may comprise sense electrodes of the impedance circuitry as described above. In some embodiments, ECG circuitry 138 can be connected to electrodes 112A and 112D so as to increase spacing of the electrodes. The inner electrodes may be positioned near the outer electrodes to increase the voltage of the ECG signal measured by ECG circuitry 138. In many embodiments, the ECG circuitry may measure the ECG signal from electrodes 112A and 112D when current is not passed through electrodes 112A and 112D, for example with switches as described in U.S. application Ser. No. 60/972,527, the full disclosure of which has been previously incorporated herein by reference.

FIG. 1D2 shows an adherent device, for example adherent device 100, positioned on patient P to determine orientation of the adherent patch. X-axis 112X of device 100 is inclined at an angle α to horizontal axis Px of patient P. Z-axis 112Z of device 100 is inclined at angle α to vertical axis Pz of patient P. Y-axis 112Y may be inclined at a second angle, for example α, to anterior posterior axis Py and vertical axis Pz. As the accelerometer of adherent device 100 can be sensitive to gravity, inclination of the patch relative to axis of the patient can be measured, for example when the patient stands.

ECG circuitry 138 can be coupled to the electrodes in many ways to define an electrocardiogram vector. For example electrode 112A can be coupled to a positive amplifier terminal of ECG circuitry 138 and electrode 112D can be coupled to a negative amplifier terminal of ECG circuitry 138 to define an orientation of an electrocardiogram vector along the electrode measurement axis. To define an electrocardiogram vector with an opposite orientation electrode 112D can be couple to the positive amplifier terminal of ECG circuitry 138 and electrode 112A can be coupled to the negative amplifier terminal of ECG circuitry 138. The ECG circuitry may be coupled to the inner electrodes so as to define an ECG vector along a measurement axis of the inner electrodes.

FIG. 1D3 shows vectors from a 3D accelerometer to determine orientation of the measurement axis of the patch adhered on the patient. The orientation can be determined for each patch adhered to the patient. A Z-axis vector 112ZV can be measured along vertical axis 112Z with an accelerometer signal from axis 134Z of accelerometer 134A. An X-axis vector 112XV can be measured along horizontal axis 112X with an accelerometer signal from axis 134X of accelerometer 134A. Inclination angle α can be determined in response to X-axis vector 112XV and Z-axis vector 112ZV, for example with vector addition of X-axis vector 112XV and Z-axis vector 112ZV. An inclination angle α for the patch along the Y and Z axes can be similarly obtained an accelerometer signal from axis 134Y of accelerometer 134A and vector 112ZV.

FIG. 1E shows batteries 150 positioned over the flex printed circuit board and electronic components as in FIG. 1D. Batteries 150 may comprise rechargeable batteries that can be removed and/or recharged. In some embodiments, batteries 150 can be removed from the adherent patch and recharged and/or replaced.

FIG. 1F shows a top view of a cover 162 over the batteries, electronic components and flex printed circuit board as in FIGS. 1A to 1E. In many embodiments, an electronics housing 160 may be disposed under cover 162 to protect the electronic components, and in some embodiments electronics housing 160 may comprise an encapsulant over the electronic components and PCB. In some embodiments, cover 162 can be adhered to adherent patch 110 with an adhesive 164 on an underside of cover 162. In many embodiments, electronics housing 160 may comprise a water proof material, for example a sealant adhesive such as epoxy or silicone coated over the electronics components and/or PCB. In some embodiments, electronics housing 160 may comprise metal and/or plastic. Metal or plastic may be potted with a material such as epoxy or silicone.

Cover 162 may comprise many known biocompatible cover, casing and/or housing materials, such as elastomers, for example silicone. The elastomer may be fenestrated to improve breathability. In some embodiments, cover 162 may comprise many known breathable materials, for example polyester, polyamide, nylon and/or elastane (Spandex™). The breathable fabric may be coated to make it water resistant, waterproof, and/or to aid in wicking moisture away from the patch.

FIG. 1G shows a side view of adherent device 100 as in FIGS. 1A to 1F. Adherent device 100 comprises a maximum dimension, for example a length 170 from about 4 to 10 inches (from about 100 mm to about 250 mm), for example from about 6 to 8 inches (from about 150 mm to about 200 mm). In some embodiments, length 170 may be no more than about 6 inches (no more than about 150 mm). Adherent device 100 comprises a thickness 172. Thickness 172 may comprise a maximum thickness along a profile of the device. Thickness 172 can be from about 0.2 inches to about 0.6 inches (from about 5 mm to about 15 mm), from about 0.2 inches to about 0.4 inches (from about 5 mm to about 10 mm), for example about 0.3 inches (about 7.5 mm).

FIG. 1H shown a bottom isometric view of adherent device 100 as in FIGS. 1A to 1G. Adherent device 100 comprises a width 174, for example a maximum width along a width profile of adherent device 100. Width 174 can be from about 2 to about 4 inches (from about 50 mm to 100 mm), for example about 3 inches (about 75 mm).

FIGS. 1I and 1J show a side cross-sectional view and an exploded view, respectively, of adherent device 100 as in FIGS. 1A to 1H. Device 100 comprises several layers. Gel 114A, or gel layer, is positioned on electrode 112A to provide electrical conductivity between the electrode and the skin. Electrode 112A may comprise an electrode layer. Adherent patch 110 may comprise a layer of breathable tape 110T, for example a known breathable tape, such as tricot-knit polyester fabric. An adhesive 116A, for example a layer of acrylate pressure sensitive adhesive, can be disposed on underside 110A of adherent patch 110.

FIGS. 1I1 and 1J1 show a side cross-sectional view and an exploded view, respectively, of embodiments of the adherent device with a temperature sensor affixed to the gel cover. In these embodiments, gel cover 180 extends over a wider area than in the embodiments shown in FIGS. 1I and 1J. Temperature sensor 177 is disposed over a peripheral portion of gel cover 180. Temperature sensor 177 can be affixed to gel cover 180 such that the temperature sensor can move when the gel cover stretches and tape stretch with the skin of the patient. Temperature sensor 177 may be coupled to temperature sensor circuitry 144 through a flex connection comprising at least one of wires, shielded wires, non-shielded wires, a flex circuit, or a flex PCB. This coupling of the temperature sensor allows the temperature near the skin to be measured though the breathable tape and the gel cover. The temperature sensor can be affixed to the breathable tape, for example through a cutout in the gel cover with the temperature sensor positioned away from the gel pads. A heat flux sensor can be positioned near the temperature sensor, for example to measure heat flux through to the gel cover, and the heat flux sensor coupled to heat flux circuitry similar to the temperature sensor.

The adherent device comprises electrodes 112A1, 112B1, 112C1 and 112D1 configured to couple to tissue through apertures in the breathable tape 110T. Electrodes 112A1, 112B1, 112C1 and 112D1 can be fabricated in many ways. For example, electrodes 112A1, 112B1, 112C1 and 112D1 can be printed on a flexible connector 112F, such as silver ink on polyurethane. Breathable tape 110T comprise apertures 180A1, 180B1, 180C1 and 180D1. Electrodes 112A1, 112B1, 112C1 and 112D1 are exposed to the gel through apertures 180A1, 180B1, 180C1 and 180D1 of breathable tape 110T. Gel 114A, gel 114B, gel 114C and gel 114D can be positioned over electrodes 112A1, 112B1, 112C1 and 112D1 and the respective portions of breathable tape 110T proximate apertures 180A1, 180B1, 180C1 and 180D1, so as to couple electrodes 112A1, 112B1, 112C1 and 112D1 to the skin of the patient. The flexible connector 112F comprising the electrodes can extend from under the gel cover to the printed circuit board to connect to the printed circuit boards and/or components supported thereon. For example, flexible connector 112F may comprise flexible connector 122A to provide strain relief, as described above.

In many embodiments, gel 114A, or gel layer, comprises a hydrogel that is positioned on electrode 112A to provide electrical conductivity between the electrode and the skin. In many embodiments, gel 114A comprises a hydrogel that provides a conductive interface between skin and electrode, so as to reduce impedance between electrode/skin interface. In many embodiments, gel may comprise water, glycerol, and electrolytes, pharmacological agents, such as beta blockers, ace inhibitors, diuretics, steroid for inflammation, antibiotic, antifungal agent. In specific embodiments the gel may comprise cortisone steroid. The gel layer may comprise many shapes, for example, square, circular, oblong, star shaped, many any polygon shapes. In specific embodiments, the gel layer may comprise at least one of a square or circular geometry with a dimension in a range from about 0.005″ to about 0.100″, for example within a range from about 0.015″-0.070″, in some embodiments within a range from about 0.015″-0.040″, and in specific embodiments within a range from about 0.020″-0.040″. In many embodiments, the gel layer of each electrode comprises an exposed surface area to contact the skin within a range from about 100 mm^2 to about 1500 mm^2, for example a range from about 250 mm^2 to about 750 mm^2, and in specific embodiments within a range from about 350 mm^2 to about 650 mm^2. Work in relation with embodiments of the present invention suggests that such dimensions and/or exposed surface areas can provide enough gel area for robust skin interface without excessive skin coverage. In many embodiments, the gel may comprise an adhesion to skin, as may be tested with a 1800 degree peel test on stainless steel, of at least about 3 oz/in, for example an adhesion within a range from about 5-10 oz/in. In many embodiments, a spacing between gels is at least about 5 mm, for example at least about 10 mm. Work in relation to embodiments of the present invention suggests that this spacing may inhibit the gels from running together so as to avoid crosstalk between the electrodes. In many embodiments, the gels comprise a water content within a range from about 20% to about 30%, a volume resistivity within a range from about 500 to 2000 ohm-cm, and a pH within a range from about 3 to about 5.

In many embodiments, the electrodes, for example electrodes 112A to 112D, may comprise an electrode layer. A 0.001″-0.005″ polyester strip with silver ink for traces can extend to silver/silver chloride electrode pads. In many embodiments, the electrodes can provide electrical conduction through hydrogel to skin, and in some embodiments may be coupled directly to the skin. Although at least 4 electrodes are shown, some embodiments comprise at least two electrodes, for example 2 electrodes. In some embodiments, the electrodes may comprise at least one of carbon-filled ABS plastic, silver, nickel, or electrically conductive acrylic tape. In specific embodiments, the electrodes may comprise at least one of carbon-filled ABS plastic, Ag/AgCl. The electrodes may comprise many geometric shapes to contact the skin, for example at least one of square, circular, oblong, star shaped, polygon shaped, or round. In specific embodiments, a dimension across a width of each electrodes is within a range from about 002″ to about 0.050″, for example from about 0.010 to about 0.040″. In many a surface area of the electrode toward the skin of the patient is within a range from about 25 mm^2 to about 1500 mm^2, for example from about 75 mm^2 to about 150 mm^2. In many embodiments, the electrode comprises a tape that may cover the gel near the skin of the patient. In specific embodiments, the two inside electrodes may comprise force, or current electrodes, with a center to center spacing within a range from about 20 to about 50 mm. In specific embodiments, the two outside electrodes may comprise measurement electrodes, for example voltage electrodes, and a center-center spacing between adjacent voltage and current electrodes is within a range from about 15 mm to about 35 mm. Therefore, in many embodiments, a spacing between inner electrodes may be greater than a spacing between an inner electrode and an outer electrode.

In many embodiments, adherent patch 110 may comprise a layer of breathable tape 110T, for example a known breathable tape, such as tricot-knit polyester fabric. In many embodiments, breathable tape 110T comprises a backing material, or backing 111, with an adhesive. In many embodiments, the patch adheres to the skin of the patient's body, and comprises a breathable material to allow moisture vapor and air to circulate to and from the skin of the patient through the tape. In many embodiments, the backing is conformable and/or flexible, such that the device and/or patch does not become detached with body movement. In many embodiments, backing can sufficiently regulate gel moisture in absence of gel cover. In many embodiments, adhesive patch may comprise from 1 to 2 pieces, for example 1 piece. In many embodiments, adherent patch 110 comprises pharmacological agents, such as at least one of beta blockers, ace inhibitors, diuretics, steroid for inflammation, antibiotic, or antifungal agent. In specific embodiments, patch 110 comprises cortisone steroid. Patch 110 may comprise many geometric shapes, for example at least one of oblong, oval, butterfly, dogbone, dumbbell, round, square with rounded corners, rectangular with rounded corners, or a polygon with rounded corners. In specific embodiments, a geometric shape of patch 110 comprises at least one of an oblong, an oval or round. In many embodiments, the geometric shape of the patch comprises a radius on each corner that is no less than about one half a width and/or diameter of tape. Work in relation to embodiments of the present invention suggests that rounding the corner can improve adherence of the patch to the skin for an extended period of time because sharp corners, for example right angle corners, can be easy to peel. In specific embodiments, a thickness of adherent patch 110 is within a range from about 0.001″ to about 0.020″, for example within a range from about 0.005″ to about 0.010″. Work in relation to embodiments of the present invention indicates that these ranges of patch thickness can improve adhesion of the device to the skin of the patient for extended periods as a thicker adhesive patch, for example tape, may peel more readily. In many embodiments, length 170 of the patch is within a range from about 2″ to about 10″, width 174 of the patch is within a range from about 1″ to about 5″. In specific embodiments, length 170 is within a range from about 4″ to about 8″ and width 174 is within a range from about 2″ to about 4″. In many embodiments, an adhesion to the skin, as measured with a 180 degree peel test on stainless steel, can be within a range from about 10 to about 100 oz/in width, for example within a range from about 30 to about 70 oz/in width. Work in relation to embodiments of the present invention suggests that adhesion within these ranges may improve the measurement capabilities of the patch because if the adhesion is too low, patch will not adhere to the skin of the patient for a sufficient period of time and if the adhesion is too high, the patch may cause skin irritation upon removal. In many embodiments adherent patch 110 comprises a moisture vapor transmission rate (MVTR, g/m^2/24 hrs) per American Standard for Testing and Materials E-96 (ASTM E-96) is at least about 400, for example at least about 1000. Work in relation to embodiments of the present invention suggest that MVTR values as specified above can provide improved comfort, for example such that in many embodiments skin does not itch. In some embodiments, the breathable tape 110T of adherent patch 110 may comprise a porosity (sec./100 cc/in2) within a wide range of values, for example within a range from about 0 to about 200. The porosity of breathable tape 110T may be within a range from about 0 to about 5. The above amounts of porosity can minimize itching of the patient's skin when the patch is positioned on the skin of the patient. In many embodiments, the MVTR values above may correspond to a MVTR through both the gel cover and the breathable tape. The above MVTR values may also correspond to an MVTR through the breathable tape, the gel cover and the breathable cover. The MVTR can be selected to minimize patient discomfort, for example itching of the patient's skin.

In some embodiments, the breathable tape may contain and elute a pharmaceutical agent, such as an antibiotic, anti-inflammatory or antifungal agent, when the adherent device is placed on the patient.

In many embodiments, tape 110T of adherent patch 110 may comprise backing material, or backing 111, such as a fabric configured to provide properties of patch 110 as described above. In many embodiments backing 111 provides structure to breathable tape 110T, and many functional properties of breathable tape 110T as described above. In many embodiments, backing 111 comprises at least one of polyester, polyurethane, rayon, nylon, breathable plastic film; woven, nonwoven, spun lace, knit, film, or foam. In specific embodiments, backing 111 may comprise polyester tricot knit fabric. In many embodiments, backing 111 comprises a thickness within a range from about 0.0005″ to about 0.020″, for example within a range from about 0.005″ to about 0.010″.

In many embodiments, an adhesive 116A, for example breathable tape adhesive comprising a layer of acrylate pressure sensitive adhesive, can be disposed on underside 110A of patch 110. In many embodiments, adhesive 116A adheres adherent patch 110 comprising backing 111 to the skin of the patient, so as not to interfere with the functionality of breathable tape, for example water vapor transmission as described above. In many embodiments, adhesive 116A comprises at least one of acrylate, silicone, synthetic rubber, synthetic resin, hydrocolloid adhesive, pressure sensitive adhesive (PSA), or acrylate pressure sensitive adhesive. In many embodiments, adhesive 116A comprises a thickness from about 0.0005″ to about 0.005″, in specific embodiments no more than about 0.003″. Work in relation to embodiments of the present invention suggests that these thicknesses can allow the tape to breathe and/or transmit moisture, so as to provide patient comfort.

A gel cover 180, or gel cover layer, for example a polyurethane non-woven tape, can be positioned over patch 110 comprising the breathable tape. A PCB layer, for example flex printed circuit board 120, or flex PCB layer, can be positioned over gel cover 180 with electronic components 130 connected and/or mounted to flex printed circuit board 120, for example mounted on flex PCB so as to comprise an electronics layer disposed on the flex PCB layer. In many embodiments, the adherent device may comprise a segmented inner component, for example the PCB may be segmented to provide at least some flexibility. In many embodiments, the electronics layer may be encapsulated in electronics housing 160 which may comprise a waterproof material, for example silicone or epoxy. In many embodiments, the electrodes are connected to the PCB with a flex connection, for example trace 123A of flex printed circuit board 120, so as to provide strain relive between the electrodes 112A, 112B, 112C and 112D and the PCB.

Gel cover 180 can inhibit flow of gel 114A and liquid. In many embodiments, gel cover 180 can inhibit gel 114A from seeping through breathable tape 110T to maintain gel integrity over time. Gel cover 180 can also keep external moisture from penetrating into gel 114A. For example gel cover 180 can keep liquid water from penetrating though the gel cover into gel 114A, while allowing moisture vapor from the gel, for example moisture vapor from the skin, to transmit through the gel cover. The gel cover may comprise a porosity at least 200 sec./100 cc/in2, and this porosity can ensure that there is a certain amount of protection from external moisture for the hydrogel.

In many embodiments, the gel cover can regulate moisture of the gel near the electrodes so as to keeps excessive moisture, for example from a patient shower, from penetrating gels near the electrodes. In many embodiments, the gel cover may avoid release of excessive moisture form the gel, for example toward the electronics and/or PCB modules. Gel cover 180 may comprise at least one of a polyurethane, polyethylene, polyolefin, rayon, PVC, silicone, non-woven material, foam, or a film. In many embodiments gel cover 180 may comprise an adhesive, for example a acrylate pressure sensitive adhesive, to adhere the gel cover to adherent patch 110. In specific embodiments gel cover 180 may comprise a polyurethane film with acrylate pressure sensitive adhesive. In many embodiments, a geometric shape of gel cover 180 comprises at least one of oblong, oval, butterfly, dogbone, dumbbell, round, square, rectangular with rounded corners, or polygonal with rounded corners. In specific embodiments, a geometric shape of gel cover 180 comprises at least one of oblong, oval, or round. In many embodiments, a thickness of gel cover is within a range from about 0.0005″ to about 0.020″, for example within a range from about 0.0005 to about 0.010″. In many embodiments, gel cover 180 can extend outward from about 0-20 mm from an edge of gels, for example from about 5-15 mm outward from an edge of the gels.

In many embodiments, the breathable tape of adherent patch 110 comprises a first mesh with a first porosity and gel cover 180 comprises a breathable tape with a second porosity, in which the second porosity is less than the first porosity to inhibit flow of the gel through the breathable tape.

In many embodiments, device 100 includes a printed circuitry, for example a printed circuitry board (PCB) module that includes at least one PCB with electronics component mounted thereon on and the battery, as described above. In many embodiments, the PCB module comprises two rigid PCB modules with associated components mounted therein, and the two rigid PCB modules are connected by flex circuit, for example a flex PCB. In specific embodiments, the PCB module comprises a known rigid FR4 type PCB and a flex PCB comprising known polyimide type PCB. In specific embodiments, the PCB module comprises a rigid PCB with flex interconnects to allow the device to flex with patient movement. The geometry of flex PCB module may comprise many shapes, for example at least one of oblong, oval, butterfly, dogbone, dumbbell, round, square, rectangular with rounded corners, or polygon with rounded corners. In specific embodiments the geometric shape of the flex PCB module comprises at least one of dogbone or dumbbell. The PCB module may comprise a PCB layer with flex PCB 120 can be positioned over gel cover 180 and electronic components 130 connected and/or mounted to flex PCB 120 so as to comprise an electronics layer disposed on the flex PCB. In many embodiments, the adherent device may comprise a segmented inner component, for example the PCB, for limited flexibility. The printed circuit may comprise polyester film with silver traces printed thereon.

In many embodiments, the electronics layer may be encapsulated in electronics housing 160. Electronics housing 160 may comprise an encapsulant, such as a dip coating, which may comprise a waterproof material, for example silicone and/or epoxy. In many embodiments, the PCB encapsulant protects the PCB and/or electronic components from moisture and/or mechanical forces. The encapsulant may comprise silicone, epoxy, other adhesives and/or sealants. In some embodiments, the electronics housing may comprising metal and/or plastic housing and potted with aforementioned sealants and/or adhesives.

In many embodiments, the electrodes are connected to the PCB with a flex connection, for example trace 123A of flex PCB 120, so as to provide strain relive between the electrodes 112A, 112B, 112C and 112D and the PCB. In such embodiments, motion of the electrodes relative to the electronics modules, for example rigid PCB's 120A, 120B, 120C and 120D with the electronic components mounted thereon, does not compromise integrity of the electrode/hydrogel/skin contact. In some embodiments, the electrodes can be connected to the PCB and/or electronics module with a flex PCB 120, such that the electrodes and adherent patch can move independently from the PCB module. In many embodiments, the flex connection comprises at least one of wires, shielded wires, non-shielded wires, a flex circuit, or a flex PCB. In specific embodiments, the flex connection may comprise insulated, non-shielded wires with loops to allow independent motion of the PCB module relative to the electrodes.

In specific embodiments, cover 162 comprises at least one of polyester, 5-25% elastane/spandex, polyamide fabric; silicone, a polyester knit, a polyester knit without elastane, or a thermoplastic elastomer. In many embodiments cover 162 comprises at least 400% elongation. In specific embodiments, cover 162 comprises at least one of a polyester knit with 10-20% spandex or a woven polyamide with 10-20% spandex. In many embodiments, cover 162 comprises a water repellent coating and/or layer on outside, for example a hydrophobic coating, and a hydrophilic coating on inside to wick moisture from body. In many embodiments the water repellent coating on the outside comprises a stain resistant coating. Work in relation to embodiments of the present invention suggests that these coatings can be important to keep excessive moisture from the gels near the electrodes and to remove moisture from body so as to provide patient comfort.

In many embodiments, cover 162 can encase the flex PCB and/or electronics and can be adhered to at least one of the electronics, the flex PCB or adherent patch 110, so as to protect at least the electronics components and the PCB. Cover 162 can attach to adherent patch 110 with adhesive 116B. Cover 162 can comprise many known biocompatible cover materials, for example silicone. Cover 162 can comprise an outer polymer cover to provide smooth contour without limiting flexibility. In many embodiments, cover 162 may comprise a breathable fabric. Cover 162 may comprise many known breathable fabrics, for example breathable fabrics as described above. In some embodiments, the breathable cover may comprise a breathable water resistant cover. In some embodiments, the breathable fabric may comprise polyester, nylon, polyamide, and/or elastane (Spandex™) to allow the breathable fabric to stretch with body movement. In some embodiments, the breathable tape may contain and elute a pharmaceutical agent, such as an antibiotic, anti-inflammatory or antifungal agent, when the adherent device is placed on the patient.

The breathable cover 162 and adherent patch 110 comprise breathable tape can be configured to couple continuously for at least one week the at least one electrode to the skin so as to measure breathing of the patient. The breathable tape may comprise the stretchable breathable material with the adhesive and the breathable cover may comprises a stretchable breathable material connected to the breathable tape, as described above, such that both the adherent patch and cover can stretch with the skin of the patient. The breathable cover may also comprise a water resistant material. Arrows 182 show stretching of adherent patch 110, and the stretching of adherent patch can be at least two dimensional along the surface of the skin of the patient. As noted above, connectors 122A, 122B, 122C and 122D between PCB 130 and electrodes 112A, 112B, 112C and 112D may comprise insulated wires that provide strain relief between the PCB and the electrodes, such that the electrodes can move with the adherent patch as the adherent patch comprising breathable tape stretches. Arrows 184 show stretching of cover 162, and the stretching of the cover can be at least two dimensional along the surface of the skin of the patient.

Cover 162 can be attached to adherent patch 110 with adhesive 116B such that cover 162 stretches and/or retracts when adherent patch 110 stretches and/or retracts with the skin of the patient. For example, cover 162 and adherent patch 110 can stretch in two dimensions along length 170 and width 174 with the skin of the patient, and stretching along length 170 can increase spacing between electrodes. Stretching of the cover and adherent patch 110, for example in two dimensions, can extend the time the patch is adhered to the skin as the patch can move with the skin such that the patch remains adhered to the skin. Electronics housing 160 can be smooth and allow breathable cover 162 to slide over electronics housing 160, such that motion and/or stretching of cover 162 is slidably coupled with housing 160. The printed circuit board can be slidably coupled with adherent patch 110 that comprises breathable tape 110T, such that the breathable tape can stretch with the skin of the patient when the breathable tape is adhered to the skin of the patient, for example along two dimensions comprising length 170 and width 174.

The stretching of the adherent device 100 along length 170 and width 174 can be characterized with a composite modulus of elasticity determined by stretching of cover 162, adherent patch 110 comprising breathable tape 110T and gel cover 180. For the composite modulus of the composite fabric cover-breathable tape-gel cover structure that surrounds the electronics, the composite modulus may comprise no more than about 1 MPa, for example no more than about 0.3 MPa at strain of no more than about 5%. These values apply to any transverse direction against the skin.

The stretching of the adherent device 100 along length 170 and width 174, may also be described with a composite stretching elongation of cover 162, adherent patch 110 comprising breathable tape breathable tape 110T and gel cover 180. The composite stretching elongation may comprise a percentage of at least about 10% when 3 kg load is a applied, for example at least about 100% when the 3 kg load applied. These percentages apply to any transverse direction against the skin.

The printed circuit board may be adhered to the adherent patch 110 comprising breathable tape 110T at a central portion, for example a single central location, such that adherent patch 110 can stretch around this central region. The central portion can be sized such that the adherence of the printed circuit board to the breathable tape does not have a substantial effect of the modulus of the composite modulus for the fabric cover, breathable tape and gel cover, as described above. For example, the central portion adhered to the patch may be less than about 100 mm2, for example with dimensions of approximately 10 mm by 10 mm (about 0.5″ by 0.5″). Such a central region may comprise no more than about 10% of the area of patch 110, such that patch 110 can stretch with the skin of the patient along length 170 and width 174 when the patch is adhered to the patient.

The cover material may comprise a material with a low recovery, which can minimize retraction of the breathable tape from the pulling by the cover. Suitable cover materials with a low recovery include at least one of polyester or nylon, for example polyester or nylon with a loose knit. The recovery of the cover material may be within a range from about 0% recovery to about 25% recovery. Recovery can refer to the percentage of retraction the cover material that occurs after the material has been stretched from a first length to a second length. For example, with 25% recovery, a cover that is stretched from a 4 inch length to a 5 inch length will retract by 25% to a final length of 4.75 inches.

Electronics components 130 can be affixed to printed circuit board 120, for example with solder, and the electronics housing can be affixed over the PCB and electronics components, for example with dip coating, such that electronics components 130, printed circuit board 120 and electronics housing 160 are coupled together. Electronics components 130, printed circuit board 120, and electronics housing 160 are disposed between the stretchable breathable material of adherent patch 110 and the stretchable breathable material of cover 160 so as to allow the adherent patch 110 and cover 160 to stretch together while electronics components 130, printed circuit board 120, and electronics housing 160 do not stretch substantially, if at all. This decoupling of electronics housing 160, printed circuit board 120 and electronic components 130 can allow the adherent patch 110 comprising breathable tape to move with the skin of the patient, such that the adherent patch can remain adhered to the skin for an extended time of at least one week, for example two or more weeks.

An air gap 169 may extend from adherent patch 110 to the electronics module and/or PCB, so as to provide patient comfort. Air gap 169 allows adherent patch 110 and breathable tape 110T to remain supple and move, for example bend, with the skin of the patient with minimal flexing and/or bending of printed circuit board 120 and electronic components 130, as indicated by arrows 186. Printed circuit board 120 and electronics components 130 that are separated from the breathable tape 110T with air gap 169 can allow the skin to release moisture as water vapor through the breathable tape, gel cover, and breathable cover. This release of moisture from the skin through the air gap can minimize, and even avoid, excess moisture, for example when the patient sweats and/or showers.

The breathable tape of adherent patch 110 may comprise a first mesh with a first porosity and gel cover 180 may comprise a breathable tape with a second porosity, in which the second porosity is less than the first porosity to minimize, and even inhibit, flow of the gel through the breathable tape. The gel cover may comprise a polyurethane film with the second porosity.

Cover 162 may comprise many shapes. In many embodiments, a geometry of cover 162 comprises at least one of oblong, oval, butterfly, dogbone, dumbbell, round, square, rectangular with rounded corners, or polygonal with rounded corners. In specific embodiments, the geometric of cover 162 comprises at least one of an oblong, an oval or a round shape.

Cover 162 may comprise many thicknesses and/or weights. In many embodiments, cover 162 comprises a fabric weight: within a range from about 100 to about 200 g/m^2, for example a fabric weight within a range from about 130 to about 170 g/m^2.

In many embodiments, cover 162 can attach the PCB module to adherent patch 110 with cover 162, so as to avoid interaction of adherent patch 110C with the PCB having the electronics mounted therein. Cover 162 can be attached to breathable tape 110T and/or electronics housing 160 comprising over the encapsulated PCB. In many embodiments, adhesive 116B attaches cover 162 to adherent patch 110. In many embodiments, cover 162 attaches to adherent patch 110 with adhesive 116B, and cover 162 is adhered to the PCB module with an adhesive 161 on the upper surface of the electronics housing. Thus, the PCB module can be suspended above the adherent patch via connection to cover 162, for example with a gap 169 between the PCB module and adherent patch. In many embodiments, gap 169 permits air and/or water vapor to flow between the adherent patch and cover, for example through adherent patch 110 and cover 162, so as to provide patient comfort.

In many embodiments, adhesive 116B is configured such that adherent patch 110 and cover 162 can be breathable from the skin to above cover 162 and so as to allow moisture vapor and air to travel from the skin to outside cover 162. In many embodiments, adhesive 116B is applied in a pattern on adherent patch 110 such that the patch and cover can be flexible so as to avoid detachment with body movement. Adhesive 116B can be applied to upper side 110B of patch 110 and comprise many shapes, for example a continuous ring, dots, dashes around the perimeter of adherent patch 110 and cover 162. Adhesive 116B may comprise at least one of acrylate, silicone, synthetic rubber, synthetic resin, pressure sensitive adhesive (PSA), or acrylate pressure sensitive adhesive. Adhesive 16B may comprise a thickness within a range from about 0.0005″ to about 0.005″, for example within a range from about 0.001-0.005″. In many embodiments, adhesive 116B comprises a width near the edge of patch 110 and/or cover 162 within a range from about 2 to about 15 mm, for example from about 3 to about 7 near the periphery. In many embodiments with such widths and/or thickness near the edge of the patch and/or cover, the tissue adhesion may be at least about 30 oz/in, for example at least about 40 oz/in, such that the cover remains attached to the adhesive patch when the patient moves.

In many embodiments, the cover is adhered to adherent patch 110 comprising breathable tape 110T at least about 1 mm away from an outer edge of adherent patch 110. This positioning protects the adherent patch comprising breathable tape 110T from peeling away from the skin and minimizes edge peeling, for example because the edge of the patch can be thinner. In some embodiments, the edge of the cover may be adhered at the edge of the adherent patch, such that the cover can be slightly thicker at the edge of the patch which may, in some instances, facilitate peeling of the breathable tape from the skin of the patient.

Gap 169 extend from adherent patch 110 to the electronics module and/or PCB a distance within a range from about 0.25 mm to about 4 mm, for example within a range from about 0.5 mm to about 2 mm.

In many embodiments, the adherent device comprises a patch component and at least one electronics module. The patch component may comprise adherent patch 110 comprising the breathable tape with adhesive coating 116A, at least one electrode, for example electrode 114A and gel 114. The at least one electronics module can be separable from the patch component. In many embodiments, the at least one electronics module comprises the flex printed circuit board 120, electronic components 130, electronics housing 160 and cover 162, such that the flex printed circuit board, electronic components, electronics housing and cover are reusable and/or removable for recharging and data transfer, for example as described above. In many embodiments, adhesive 116B is coated on upper side 110A of adherent patch 110B, such that the electronics module can be adhered to and/or separated from the adhesive component. In specific embodiments, the electronic module can be adhered to the patch component with a releasable connection, for example with Velcro™, a known hook and loop connection, and/or snap directly to the electrodes. Two electronics modules can be provided, such that one electronics module can be worn by the patient while the other is charged, as described above. Monitoring with multiple adherent patches for an extended period is described in U.S. Pat. App. No. 60/972,537, the full disclosure of which has been previously incorporated herein by reference. Many patch components can be provided for monitoring over the extended period. For example, about 12 patches can be used to monitor the patient for at least 90 days with at least one electronics module, for example with two reusable electronics modules.

In many embodiments, the adherent device comprises a patch component and at least one electronics module. The patch component may comprise adherent patch 110 comprising the breathable tape with adhesive coating 116A, at least one electrode, for example electrode 114A and gel 114. The at least one electronics module can be separable from the patch component. In many embodiments, the at least one electronics module comprises the flex printed circuit board 120, electronic components 130, electronics housing 160 and cover 162, such that the flex printed circuit board, electronic components, electronics housing and cover are reusable and/or removable for recharging and data transfer, for example as described above. In many embodiments, adhesive 116B is coated on upper side 110A of adherent patch 110B, such that the electronics module can be adhered to and/or separated from the adhesive component. In specific embodiments, the electronic module can be adhered to the patch component with a releasable connection, for example with Velcro™, a known hook and loop connection, and/or snap directly to the electrodes. Two electronics modules can be provided, such that one electronics module can be worn by the patient while the other is charged, as described above. Monitoring with multiple adherent patches for an extended period is described in U.S. Pat. App. No. 60/972,537, the full disclosure of which has been previously incorporated herein by reference. Many patch components can be provided for monitoring over the extended period. For example, about 12 patches can be used to monitor the patient for at least 90 days with at least one electronics module, for example with two reusable electronics modules.

At least one electrode 112A can extend through at least one aperture 180A in the breathable tape 110 and gel cover 180.

In some embodiments, the adhesive patch may comprise a medicated patch that releases a medicament, such as antibiotic, beta-blocker, ACE inhibitor, diuretic, or steroid to reduce skin irritation. The adhesive patch may comprise a thin, flexible, breathable patch with a polymer grid for stiffening. This grid may be anisotropic, may use electronic components to act as a stiffener, may use electronics-enhanced adhesive elution, and may use an alternating elution of adhesive and steroid.

FIG. 1K shows at least one electrode 190 configured to electrically couple to a skin of the patient through a breathable tape 192. In many embodiments, at least one electrode 190 and breathable tape 192 comprise electrodes and materials similar to those described above. Electrode 190 and breathable tape 192 can be incorporated into adherent devices as described above, so as to provide electrical coupling between the skin an electrode through the breathable tape, for example with the gel.

FIG. 2 shows a method 200 of monitoring a person.

A step 205 adheres a measurement device to patient to measure heart rate, activity, body posture, respiration rate and bioimpedance. The adherent device may comprise an adherent device as described above. The device may comprise ECG circuitry to measure the HR, an accelerometer to measure patient activity and orientation, impedance circuitry to measure breathing and patient hydration. Additional or alternative sensors can be used. For example, breathing may be determined with a sensor that provides a signal in response to expansion of the chest and expansion of the skin of the patient.

A step 210 measures, stores and processes patient data with adherent device. The adherent device may measure HR, patient activity and orientation, breathing and hydration, and these data can be stored on the adherent device, for example stored on the processor at least prior to communication with the gateway. The processor may determine a heart rate of the patient based on the ECG and may determine hydration and breathing based on an impedance signal from the impedance circuitry, for example.

A step 212 determines patient drug treatment. The drug treatment can be determined based on a prescription from a physician, for example.

A step 215 transmits patient data from adherent device to the gateway, as described above. A step 220 receives the patient data with the gateway.

A step 225 measures, stores and processes patient data with gateway. The gateway can store data of the adherent device and process the data. For example, the gateway can perform one or more of the steps of sub-steps so as to identify the CI. Also, the gateway may comprise at least one sensor to measure additional patient data, and may also combine data with data from additional measurement devices.

A step 230 transmits patient data from the gateway to the remote server.

A step 235 stores and processed patient data with remote server. The remoter server can store data of the adherent device and process the data. For example, the remote server can perform one or more of the steps of sub-steps so as to identify the CI.

A step 240 identifies functional CI with profile of remote heart rates. This functional CI can be identified in many ways, for example with one or more measurement of location of the heart rate data, measures of dispersion and variability of the heart rate data, skewness and kurtosis of the heart rate data, or comparison of portions around a mode of a single modal mounded distribution.

A sub-step 241—determines a profile of remote heart rates. A sub-step 242 determines a peak of the profile of remote heart rates. For example, the profile may comprise a histogram or Gaussian probability function and the peak may comprise the mode of the distribution or probability function. A sub-step 243 determines a portion of profile above peak. A sub-step 244 determines a portion of profile below peak. A sub-step 245 compares a portion above peak to a portion below peak. A sub-step 246 identifies functional CI when the portion above peak is less than portion below. For example, the portion above may correspond to the occurrence of heart rates above the peak hear rate and the portion below the peak may correspond to the occurrence of heart rates below the peak.

Based on the teachings described herein one can determine relevant parameters from the heart rate distribution profile so as to identify the functional CI.

A step 250 identifies functional CI with resting remote HR. A sub-step 251—determines the occurrence of heart rates corresponding to profile. A sub-step 252 determines a peak of the remote heart rates. A sub-step 253 determines the peak of remote heart rates. A sub-step 254 determine the remote resting HR based on the peak of the remote HR. A sub-step 255 determines age corrected maximum HR. A sub-step 256 determines the HRR based on age corrected maximum HR and remote resting HR. A sub-step 257 identifies functional CI when the HRR is below the threshold.

A step 260 identifies functional CI with maximum HR. A sub-step 261 determines the threshold activity amount based on patient data from a plurality of other patients, for example from a patient population measured with substantially similar adherent devices when the patients are at home. A sub-step 262 determines the patient activity above threshold. A sub-step 263 determines heart rates of the patient corresponding to patient activity above threshold. For example, the heart rate of the patient may comprise a maximum HR of the patient and the maximum HR of the patient can be compared to the threshold. A sub-step 264 determines a correlation of HR above threshold with one or more of activity, body posture, respiration rate and bioimpedance. A sub-step 265 determines patient drug treatment and compliance. A sub-step 266 determines functional CI based on patient drug treatment and correlation of HR above threshold with the one or more of activity, body posture, respiration rate or bioimpedance. A step 270 transmits notification to one or more of physician or patient based on identification of CI.

The 85% cut-off for functional CI classification can be modified to other cut-offs to account for pharmacological modification of heart rate response such as beta-blockers and other chronotropic/lusitropic medication.

The processor system, as described above, may comprise instructions of a computer program embedded thereon so as to perform many of the steps of the method 200. For example, many of the steps of the method 200 can be performed with processor system comprising the processor of the adherent device, the processor of the gateway and the processor of the remote server. The method 200 can be performed with one or more of the processor of the adherent device, the processor of the gateway and the processor of the remote server. Further the steps of the method 200 can be distributed among the processor of the processor system such that each processor performs at least one of the steps or sub-steps of method 200.

It should be appreciated that the specific steps illustrated in FIG. 2 provide a particular method of monitoring a patient and responding to a signal event, in accordance with an embodiment of the present invention. Other sequences of steps may also be performed in accordance with alternative embodiments. For example, alternative embodiments of the present invention may perform the steps outlined above in a different order. Moreover, the individual steps illustrated in FIG. 2 may include multiple sub-steps that may be performed in various sequences as appropriate to the individual step. Alternatively, the multiple sub-steps may be performed as an individual step. Furthermore, additional steps may be added or removed depending on the particular applications. One of ordinary skill in the art would recognize many variations, modifications, and alternatives.

The patient data as described above can be combined to determine the functional CI of the patient. For example, the data can be combined with one or more correlations of heart rate to one or more of the activity index (hereinafter “AI”), body posture (hereinafter “BP”), impedance of the patient (hereinafter “BioZ” or respiration rate (hereinafter “RR”). The AI may comprise an index based on the measurements from the three axes of the accelerometer as described above. The BP may comprise an angle of the patient based on orientation from accelerometer as described above. The BioZ may comprise impedance averaged over patient breathing cycles to correct for patient breathing or corrected for patient breathing with a portion of the breathing pattern. For example, the heart rate can be correlated with these data with the equation:
HR=a*AI+b*BP+c*BioZ+d*RR,
where a, b, c and d are respective correlation coefficients. The above equation is merely an example of a correlation equation as many additional equations can be used such as equations with cross terms, for example of AI with BP, and with squared terms, for example with coefficients of (BP)*(BP).

The patient data may also be combined with multi-dimensional look up tables, for example with look up tables comprising levels or tiers for each measured data parameter such as AI. For example, AI may comprise a level, or tier, based on counts of an accelerometer or other index.

Embodiments as described herein can be incorporated with many commercially available patient monitoring and treatment systems such as the OptiVol™ alert algorithm and computer programs embodying instructions thereof commercially available from Medtronic, the CareLink™ server commercially available from Medtronic, the Latitude™ patient management system commercially available from Boston Scientific, the Merlin™ system commercially available from St. Jude and the MCOT™ commercially available from CardioNet.

Experimental Clinical Studies

An experimental clinical study can be conducted on an empirical number of patients to determine empirically parameters of the above described adherent device and processor system so as to determine functional CI of the patient. The empirically determined parameters can be used with programs of the processor system to determine status of the patient, for example to determine deterioration in the status, based on the teachings as described herein.

FIGS. 3A1 to 3A5 show heart rate, activity index, body posture, impedance, and respiration rate measured from an adherent device adhered to the skin of the patient. Although the device can be adhered for at least about one week as described above, the data of FIG. 3A show at least about 24 hours of measured data to show an example of data suitable for combination. Each of FIGS. 3A1 to 3A5 have a corresponding time base, for example from a data time stamp of the processor of the adherent device.

FIG. 3A1 shows the heart rate of the patient in beats per minute from 00:00 hours to 24:00 hours. The heart rate may be determined with one or more of the processor of the adherent device, the processor of the gateway or the processor of the remote server. The HR shows an elevation at about 11:00.

FIG. 3A2 shows patient activity amounts. The patient activity amounts may comprise an index and many measures of patient activity. For example, the activity index may comprise counts and/or an arbitrary scale, and the values can range from about 0 to about 300. The data show a peak at about 11:00.

FIG. 3A3 shows patient body posture angle. The patient body posture is shown to be upright, at around 80 degrees from about 07:00 to about 18:00. These data indicate that the patient is awake and upright from about 07:00 to about 18:00.

FIG. 3A4 shows patient impedance. The patient impedance is shown to vary from about 60 to about 80 Ohms. For example with local peaks around 11:00 and 14:00 corresponding to about 74 and 78 Ohms, respectively.

FIG. 3A5 shows patient breathing rate, also referred to as patient respiration rate. The respiration rate of the patient varies from about 10 breaths per minute to about 30 breaths per minute.

Based on the teachings described herein, the instruction of the processor system can identify functional CI from the HR data and data of one or more of the other sensors. The method and instructions of the processor system can identify functional CI of the patient based on HR and one or more sensors from about 10:00 to 11:00. For example, the patient activity comprises a peak around 11 am corresponding to an activity amount above the threshold determined with similar adherent devices from a population of patients or relative to the patient's own activity mean over a given 24 hour period. For example, the threshold may correspond to an activity amount of 100, such that the patient heart rates corresponding to the activity index above the threshold of 100 correspond to maximum HR of the patient. The processor system comprises patient data including the age of the patient such that the age corrected maximum HR can be determined and the functional CI of the patient can be identified based on the age corrected maximum HR and the maximum HR of the patient. The increase in activity was not paralleled by a comparable increase in HR so as to comprise a diagnostic marker to identify CI with the remote patient measurements as described herein.

The processor system and methods described herein can identify functional CI of the patient based on the profile of the HR data, for example based on histogram as described herein.

FIG. 3B shows patient data measured remotely with an adherent device as described above. The patient data shows a distribution comprising a histogram for a first patient without functional CI and a second patient with functional CI. The patient heart rate data may comprise data measured during the day when the patient is active. The data may comprise a modal heart rate distribution. The data show a histogram for each patient. The heart rate of each patient is determined over time. The occurrence of heart rate in 5 beat per minute intervals is shown from 50 beats per minute to 140 beats per minute. The patient with no functional CI shows a peak at about 90 beats per minute, and the patient with functional CI shows a peak at about 105 beats per minute.

The functional CI can be identified with the profile of remote heart rates. This functional CI can be identified in many ways, for example with one or more measurement of location of the heart rate data, measures of dispersion and variability of the heart rate data, skewness and kurtosis of the heart rate data, or comparison of portions around a mode of a single modal mounded distribution.

The histogram distribution of each patient comprises a first side corresponding to a first amount of occurrences of heart rates below the peak and a second side corresponding to a second amount of occurrences of heart rates above the peak. The distribution of the first patient without functional CI has a first amount of occurrences below the peak at 90 bpm and a second amount of occurrence above the peak at 90 bpm, and the first amount is substantially equal to the second amount. The distribution of the second patient with functional CI has a first amount of occurrences below the peak at 105 bpm and a second amount of occurrence above the peak at 105 bpm, and the first amount is substantially greater than the second amount.

Alternatively or in combination, the histogram distribution of each patient can be fit to a Gaussian distribution and a skew of the distribution for each patient determined. For example, the first patient without functional CI comprises substantially no skew of the histogram distribution, and the second patient with functional CI comprises significant skew of the histogram distribution.

The peak of the HR data of FIG. 3B corresponds to the resting HR of the patient, such that the HRR of the patient can be calculated. The HRR can be combined with the profile from the histogram to identify the patient CI.

FIG. 3C shows average maximum activity of patients based on age for ages from about 20 to about 90. These average maximum activity levels from a population of patients can be used to determine threshold criteria and correlate activity with additional measurement parameters, such has heart rate and change in heart rate.

Clinical Studies for Remote Monitoring and Diagnosis of Chronotropic Incompetence in HF patients.

A study can be conducted to diagnose functional CI during activities of daily living, through remote monitoring, so as to provide important information for effectively managing HF and understanding the role of functional CI in contributing to HF symptoms. The study may comprise HF patients having an ejection fraction (hereinafter “EF”) of 40% or less.

Study Design: The study may comprise a prospective monitoring study of patients with chronic HF using an external multi-sensor monitor, for example an adherent monitor as described above. The study may comprise data from multiple centers and enroll approximately 200 enrolled patients with NYHA Class III/IV, EF≦40%. The wireless monitoring device can be applied to the patient's chest and replaced weekly during a 90-day monitoring period. Heart rate (HR), respiratory rate, activity level and body impedance data from the device were transmitted at regular interval via phone and used for offline analysis.

The data can be analyzed to determine results and compare the determined functional CI to similar study populations. The following can be determined for the population: gender, age, body mass index, EF, percentage of patient with beta-blockers. For each patient, the modal HR during daily activity was calculated and used to perform functional CI determination. The percentage of patients with functional CI can be determined when defined as an inability to reach 85% of age-predicted maximum HR. When adjusted for beta-blocker use, the percentage of patient having functional CI can be determined.

Applicants note that a study design as described above has been conducted on a population of approximately 300 patients.

FIG. 3D1 shows correlation of heart rate data with activity data for patients without functional CI from the study. The fit parameters are HR (bpm)=0.0985*(Activity)+75.4 (R2=0.151)

FIG. 3D2 shows correlation of heart rate data with activity data for patients with functional CI from the study. The fit parameters are HR (bpm)=0.0126*(Activity)+82.651 (R2=0.0026)

The correlations shown in FIGS. 3D1 and 3D2 are examples of linear correlations of heart rate with activity that can be determined. The correlation coefficient of the patients without functional CI shows a steeper slope for a linear fit between HR and activity when compared to patients with functional CI. The less steep curve of the patients with CI shows a blunting of heart rate response to activities of daily living, when adjusted for age. This blunting of HR elevation can be combined with additional patient measurement data, as described above.

Applicants note that the presence of functional CI in the study was determined based on measured activity above a percentage of the mean age adjusted maximum heart rate as shown above with reference to FIG. 3C. This measured activity above the threshold amount can be used to determine the presence of functional CI. Based on this crossing of measured patient activity above the threshold and the corresponding HR can be used to identify the patient as having functional CI or not having functional CI. Of approximately 300 patients, about 29% of the patients were determined to have functional CI and approximately 12% were determined to have no functional CI. For the remaining 59% of the patients, the functional CI was indeterminate based on activity and heart rate due to the sedentary status of the patient. However, Applicants note that additional patient measurement data can be used to identify the functional CI in accordance with additional steps of method 300 described above, such that the presence (or absence) of functional CI can be positively determined for a majority of patients. For example the profile of the HR distribution and the heart rate reserve of the patient as measured at home can be used to determine the presence of functional CI.

Additional correlations and correspondence among patient data can be made with additional variables as described above so as to identify functional CI in a patient population. The correlations may comprise a plurality of variables correlated with the HR profile, as described above. Look up tables can also be determined to compare functional CI with measurement data such as activity, orientation, activity, respiration rate and body temperature.

While the exemplary embodiments have been described in some detail, by way of example and for clarity of understanding, those of skill in the art will recognize that a variety of modifications, adaptations, and changes may be employed. Hence, the scope of the present invention should be limited solely by the appended claims.

Claims

1. An apparatus to monitor a patient, the apparatus comprising,

a processor system comprising at least one processor having a tangible medium with instructions of a computer program embodied thereon, the processor system configured to
receive heart rate data of the patient, the heart rate data comprising a plurality of measurements of the patient's heart rate taken over a period of time under a variety of conditions of daily living on the part of the patient, wherein the period of time encompasses a variety of activity levels on the part of the patient, and the heart rate data includes heart rates measured while the patient is at rest and heart rates measured while the patient is active;
construct a histogram of the measurements of the patient's heart rate; and
identify chronotropic incompetence of the patient based on the shape of the histogram;
wherein the computer program comprises instructions to determine a peak of the histogram and a first portion of the histogram and a second portion of the histogram, the first portion corresponding to a first amount of occurrences of first heart rates lower than the heart rate corresponding to the peak and the second portion corresponding to a second amount of occurrences of second heart rates greater than the heart rate corresponding to the peak and wherein the chronotropic incompetence is identified based on the second amount smaller than the first amount.

2. The apparatus of claim 1 wherein the computer program comprises instructions to identify the chronotropic incompetence with one or more of measures of dispersion and variability of the heart rate data, skewness and kurtosis of the heart rate data, or comparison of portions around a mode of a single modal mounded distribution.

3. The apparatus of claim 1, wherein the patient is a remote patient.

4. The apparatus of claim 1 wherein the computer program comprises instructions to receive respiration data of the patient and activity data of the patient and instructions to combine the heart rate data with the respiration data and activity data to identify the chronotropic incompetence.

5. A method of monitoring a patient, the method comprising,

measuring heart rate data of the patient, the heart rate data comprising a plurality of measurements of the patient's heart rate taken over a period of time under a variety of conditions of daily living on the part of the patient, wherein the period of time encompasses a variety of activity levels on the part of the patient, and the heart rate data includes heart rates measured while the patient is at rest and heart rates measured while the patient is active;
providing a processor system comprising at least one processor having a tangible medium with instructions of a computer program embodied thereon;
receiving, by the processor under control of the computer program, the heart rate data of the patient;
constructing, by the processor, a histogram of the measurements of the patient's heart rate;
determining a peak of the histogram and a first portion of the histogram and a second portion of the histogram, the first portion corresponding to a first amount of occurrences of first heart rates lower than the heart rate corresponding to the peak and the second portion corresponding to a second amount of occurrences of second heart rates greater than the heart rate corresponding to the peak; and
identifying chronotropic incompetence based on the second amount smaller than the first amount.

6. An apparatus to monitor a patient having a skin, the apparatus comprising,

an adherent device to measure patient data comprising wireless communication circuitry and measurement circuitry, the measurement circuitry coupled to at least two electrodes, a respiration sensor and an activity sensor, the adherent device comprising a support with an adhesive to adhere the at least two electrodes to the skin and support the wireless communication circuitry, the processor circuitry and the measurement circuitry with the skin;
a server located remote from the patient to receive the patient data; and
a gateway coupled to each of the adherent device and the server with wireless communication to transmit the patient data;
wherein one or more of the adherent device, the server or the gateway comprises at least one processor having a tangible memory medium with instructions of a computer program embodied thereon to determine a chronotropic incompetence of the patient based on the patient data measured with the at least two electrodes, the respiration sensor and the activity sensor;
and wherein the computer program comprises instructions to determine a histogram of heart rate data comprising a plurality of measurements of the patient's heart rate taken over a period of time under a variety of conditions of daily living on the part of the patient, wherein the period of time encompasses a variety of activity levels on the part of the patient, and the heart rate data includes heart rates measured while the patient is at rest and heart rates measured while the patient is active;
and wherein the at least one processor is configured to determine a peak of the histogram and a first portion of the histogram and a second portion of the histogram, the first portion corresponding to a first amount of occurrences of first heart rates lower than the heart rate corresponding to the peak and the second portion of the histogram corresponding to a second amount of occurrences of second heart rates greater than the heart rate corresponding to the peak and wherein the processor is configured to determine chronotropic incompetence based on the second amount smaller than the first amount.

7. The apparatus of claim 6 wherein the at least one processor comprises instructions to fit the histogram to a Gaussian distribution and determine a skew of the distribution and wherein the chronotropic incompetence is determined based on the skew.

8. The apparatus of claim 6 wherein the at least one processor comprises instructions to determine a distribution of heart rates of the patient, the distribution corresponding heart rates less than a maximum heart rate of the patient and wherein the at least one processor is configured to determine the chronotropic incompetence based on the distribution heart rate intervals corresponding to less than the maximum heart rate of the patient.

9. The apparatus of claim 6 wherein the at least one processor comprises instructions to determine a distribution of heart rates of the patient, the distribution corresponding to heart rates less than a maximum heart rate of the patient and wherein the at least one processor comprises instructions to determine the maximum heart rate of the patient based on the distribution heart rate intervals corresponding to less than the maximum heart rate of the patient.

10. The apparatus of claim 9 wherein the at least one processor comprises instructions to determine the chronotropic incompetence of the patient based on the maximum heart rate of the patient.

11. The apparatus of claim 6 wherein the at least one processor comprises instructions to determine the maximum heart rate of the patient based on the distribution of heart rates corresponding to less than the maximum heart rate of the patient.

12. A method of monitoring a patient, the method comprising:

measuring heart rate data of the patient, wherein the heart rate data includes a plurality of measurements of the patient's heart rate taken over a period of time under a variety of conditions of daily living on the part of the patient, wherein the period of time encompasses a variety of activity levels on the part of the patient, and the heart rate data includes heart rates measured while the patient is at rest and heart rates measured while the patient is active;
providing a processor system comprising at least one processor having a tangible medium with instructions of a computer program embodied thereon, wherein the processor system receives heart rate data measured over a period of time from the patient and determines a histogram of the heart rates and wherein the processor determines a peak of the histogram and a first portion of the histogram and a second portion of the histogram, the first portion corresponding to a first amount of occurrences of first heart rates lower than the heart rate corresponding to the peak and the second portion of the histogram corresponding to a second amount of occurrences of second heart rates greater than the heart rate corresponding to the peak and wherein the processor determines chronotropic incompetence based on the second amount smaller than the first amount.

13. The method of claim 12 the heart rate data comprise data measured from a patch adhered to the patient for at least about one week and wherein the heart rate data is transmitted with wireless communication.

Referenced Cited
U.S. Patent Documents
834261 October 1906 Chambers
2087124 July 1937 Smith et al.
2184511 December 1939 Bagno et al.
3170459 February 1965 Phipps et al.
3232291 February 1966 Parker
3370459 February 1968 Cescati
3517999 June 1970 Weaver
3620216 November 1971 Szymanski
3677260 July 1972 Funfstuck et al.
3805769 April 1974 Sessions
3845757 November 1974 Weyer
3874368 April 1975 Asrican
3882853 May 1975 Gofman et al.
3942517 March 9, 1976 Bowles et al.
3972329 August 3, 1976 Kaufman
4008712 February 22, 1977 Nyboer
4024312 May 17, 1977 Korpman
4077406 March 7, 1978 Sandhage et al.
4121573 October 24, 1978 Crovella et al.
4141366 February 27, 1979 Cross, Jr. et al.
RE30101 September 25, 1979 Kubicek et al.
4185621 January 29, 1980 Morrow
4216462 August 5, 1980 McGrath et al.
4300575 November 17, 1981 Wilson
4308872 January 5, 1982 Watson et al.
4358678 November 9, 1982 Lawrence
4409983 October 18, 1983 Albert
4450527 May 22, 1984 Sramek
4451254 May 29, 1984 Dinius et al.
4478223 October 23, 1984 Allor
4498479 February 12, 1985 Martio et al.
4522211 June 11, 1985 Bare et al.
4661103 April 28, 1987 Harman
4664129 May 12, 1987 Helzel et al.
4669480 June 2, 1987 Hoffman
4673387 June 16, 1987 Phillips et al.
4681118 July 21, 1987 Asai et al.
4692685 September 8, 1987 Blaze
4699146 October 13, 1987 Sieverding
4721110 January 26, 1988 Lampadius
4730611 March 15, 1988 Lamb
4781200 November 1, 1988 Baker
4793362 December 27, 1988 Tedner
4838273 June 13, 1989 Cartmell
4838279 June 13, 1989 Fore
4850370 July 25, 1989 Dower
4880004 November 14, 1989 Baker, Jr. et al.
4895163 January 23, 1990 Libke et al.
4911175 March 27, 1990 Shizgal
4945916 August 7, 1990 Kretschmer et al.
4955381 September 11, 1990 Way et al.
4966158 October 30, 1990 Honma et al.
4981139 January 1, 1991 Pfohl
4988335 January 29, 1991 Prindle et al.
4989612 February 5, 1991 Fore
5001632 March 19, 1991 Hall-Tipping
5012810 May 7, 1991 Strand et al.
5025791 June 25, 1991 Niwa
5027824 July 2, 1991 Dougherty et al.
5050612 September 24, 1991 Matsumura
5063937 November 12, 1991 Ezenwa et al.
5080099 January 14, 1992 Way et al.
5083563 January 28, 1992 Collins
5086781 February 11, 1992 Bookspan
5113869 May 19, 1992 Nappholz et al.
5125412 June 30, 1992 Thornton
5133355 July 28, 1992 Strand et al.
5140985 August 25, 1992 Schroeder et al.
5150708 September 29, 1992 Brooks
5168874 December 8, 1992 Segalowitz
5226417 July 13, 1993 Swedlow et al.
5241300 August 31, 1993 Buschmann
5257627 November 2, 1993 Rapoport
5271411 December 21, 1993 Ripley et al.
5273532 December 28, 1993 Niezink et al.
5282840 February 1, 1994 Hudrlik
5291013 March 1, 1994 Nafarrate et al.
5297556 March 29, 1994 Shankar
5301677 April 12, 1994 Hsung
5319363 June 7, 1994 Welch et al.
5331966 July 26, 1994 Bennett et al.
5335664 August 9, 1994 Nagashima
5343869 September 6, 1994 Pross et al.
5353793 October 11, 1994 Bornn
5362069 November 8, 1994 Hall-Tipping
5375604 December 27, 1994 Kelly et al.
5411530 May 2, 1995 Akhtar
5437285 August 1, 1995 Verrier et al.
5443073 August 22, 1995 Wang et al.
5449000 September 12, 1995 Libke et al.
5450845 September 19, 1995 Axelgaard
5454377 October 3, 1995 Dzwonczyk et al.
5464012 November 7, 1995 Falcone
5469859 November 28, 1995 Tsoglin et al.
5482036 January 9, 1996 Diab et al.
5496361 March 5, 1996 Moberg et al.
5503157 April 2, 1996 Sramek
5511548 April 30, 1996 Raizzi et al.
5511553 April 30, 1996 Segalowitz
5518001 May 21, 1996 Snell
5523742 June 4, 1996 Simkins et al.
5529072 June 25, 1996 Sramek
5544661 August 13, 1996 Davis et al.
5558638 September 24, 1996 Evers et al.
5560368 October 1, 1996 Berger
5564429 October 15, 1996 Bornn et al.
5564434 October 15, 1996 Halperin et al.
5566671 October 22, 1996 Lyons
5575284 November 19, 1996 Athan et al.
5607454 March 4, 1997 Cameron et al.
5632272 May 27, 1997 Diab et al.
5634468 June 3, 1997 Platt et al.
5642734 July 1, 1997 Ruben et al.
5673704 October 7, 1997 Marchlinski et al.
5678562 October 21, 1997 Sellers
5687717 November 18, 1997 Halpern et al.
5718234 February 17, 1998 Warden et al.
5724025 March 3, 1998 Tavori
5738107 April 14, 1998 Martinsen et al.
5748103 May 5, 1998 Flach et al.
5767791 June 16, 1998 Stoop et al.
5769793 June 23, 1998 Pincus et al.
5772508 June 30, 1998 Sugita et al.
5772586 June 30, 1998 Heinonen et al.
5778882 July 14, 1998 Raymond et al.
5788643 August 4, 1998 Feldman
5803915 September 8, 1998 Kremenchugsky et al.
5807272 September 15, 1998 Kun
5814079 September 29, 1998 Kieval et al.
5817035 October 6, 1998 Sullivan
5833603 November 10, 1998 Kovacs et al.
5836990 November 17, 1998 Li
5855614 January 5, 1999 Stevens et al.
5860860 January 19, 1999 Clayman
5862802 January 26, 1999 Bird
5862803 January 26, 1999 Besson et al.
5865733 February 2, 1999 Malinouskas et al.
5876353 March 2, 1999 Riff
5904708 May 18, 1999 Goedeke
5935079 August 10, 1999 Swanson et al.
5941831 August 24, 1999 Turcott
5944659 August 31, 1999 Flach et al.
5949636 September 7, 1999 Johnson et al.
5957854 September 28, 1999 Besson et al.
5957861 September 28, 1999 Combs et al.
5964703 October 12, 1999 Goodman et al.
5970986 October 26, 1999 Bolz et al.
5984102 November 16, 1999 Tay
5987352 November 16, 1999 Klein et al.
6007532 December 28, 1999 Netherly
6027523 February 22, 2000 Schmieding
6045513 April 4, 2000 Stone et al.
6047203 April 4, 2000 Sackner et al.
6047259 April 4, 2000 Campbell et al.
6049730 April 11, 2000 Kristbjarnarson
6050267 April 18, 2000 Nardella et al.
6050951 April 18, 2000 Friedman et al.
6052615 April 18, 2000 Feild et al.
6080106 June 27, 2000 Lloyd et al.
6081735 June 27, 2000 Diab et al.
6095991 August 1, 2000 Krausman et al.
6102856 August 15, 2000 Groff et al.
6104949 August 15, 2000 Pitts Crick et al.
6112224 August 29, 2000 Peifer et al.
6117077 September 12, 2000 Del Mar et al.
6125297 September 26, 2000 Siconolfi
6129744 October 10, 2000 Boute
6141575 October 31, 2000 Price
6144878 November 7, 2000 Schroeppel et al.
6164284 December 26, 2000 Schulman et al.
6181963 January 30, 2001 Chin et al.
6185452 February 6, 2001 Schulman et al.
6190313 February 20, 2001 Hinkle
6190324 February 20, 2001 Kieval et al.
6198394 March 6, 2001 Jacobsen et al.
6198955 March 6, 2001 Axelgaard et al.
6208894 March 27, 2001 Schulman et al.
6212427 April 3, 2001 Hoover
6213942 April 10, 2001 Flach et al.
6225901 May 1, 2001 Kail, IV
6245021 June 12, 2001 Stampfer
6259939 July 10, 2001 Rogel
6272377 August 7, 2001 Sweeney et al.
6277078 August 21, 2001 Porat et al.
6287252 September 11, 2001 Lugo
6289238 September 11, 2001 Besson et al.
6290646 September 18, 2001 Cosentino et al.
6295466 September 25, 2001 Ishikawa et al.
6305943 October 23, 2001 Pougatchev et al.
6306088 October 23, 2001 Krausman et al.
6308094 October 23, 2001 Shusterman et al.
6312378 November 6, 2001 Bardy
6315721 November 13, 2001 Schulman et al.
6327487 December 4, 2001 Stratbucker
6336903 January 8, 2002 Bardy
6339722 January 15, 2002 Heethaar et al.
6343140 January 29, 2002 Brooks
6347245 February 12, 2002 Lee et al.
6358208 March 19, 2002 Lang et al.
6385473 May 7, 2002 Haines et al.
6398727 June 4, 2002 Bui et al.
6400982 June 4, 2002 Sweeney et al.
6411853 June 25, 2002 Millot et al.
6416471 July 9, 2002 Kumar et al.
6442422 August 27, 2002 Duckert
6450820 September 17, 2002 Palsson et al.
6450953 September 17, 2002 Place et al.
6453186 September 17, 2002 Lovejoy et al.
6454707 September 24, 2002 Casscells, III et al.
6454708 September 24, 2002 Ferguson et al.
6459930 October 1, 2002 Takehara et al.
6463328 October 8, 2002 John
6473640 October 29, 2002 Erlebacher
6480733 November 12, 2002 Turcott
6480734 November 12, 2002 Zhang et al.
6490478 December 3, 2002 Zhang et al.
6491647 December 10, 2002 Bridger et al.
6494829 December 17, 2002 New, Jr. et al.
6512949 January 28, 2003 Combs et al.
6520967 February 18, 2003 Cauthen
6527711 March 4, 2003 Stivoric et al.
6527729 March 4, 2003 Turcott
6544173 April 8, 2003 West et al.
6544174 April 8, 2003 West et al.
6551251 April 22, 2003 Zuckerwar et al.
6551252 April 22, 2003 Sackner et al.
6569160 May 27, 2003 Goldin et al.
6572557 June 3, 2003 Tchou et al.
6572636 June 3, 2003 Hagen et al.
6577139 June 10, 2003 Cooper
6577893 June 10, 2003 Besson et al.
6577897 June 10, 2003 Shurubura et al.
6579231 June 17, 2003 Phipps
6580942 June 17, 2003 Willshire
6584343 June 24, 2003 Ransbury et al.
6587715 July 1, 2003 Singer
6589170 July 8, 2003 Flach et al.
6595927 July 22, 2003 Pitts-Crick et al.
6595929 July 22, 2003 Stivoric et al.
6600949 July 29, 2003 Turcott
6602201 August 5, 2003 Hepp et al.
6605038 August 12, 2003 Teller et al.
6611705 August 26, 2003 Hopman et al.
6611783 August 26, 2003 Kelly et al.
6616606 September 9, 2003 Petersen et al.
6622042 September 16, 2003 Thacker
6636754 October 21, 2003 Baura et al.
6641542 November 4, 2003 Cho et al.
6645153 November 11, 2003 Kroll et al.
6649829 November 18, 2003 Garber et al.
6650917 November 18, 2003 Diab et al.
6658300 December 2, 2003 Govari et al.
6659947 December 9, 2003 Carter et al.
6659949 December 9, 2003 Lang et al.
6687540 February 3, 2004 Marcovecchio
6697658 February 24, 2004 Al-Ali
RE38476 March 30, 2004 Diab et al.
6699200 March 2, 2004 Cao et al.
6701271 March 2, 2004 Willner et al.
6714813 March 30, 2004 Ishigooka et al.
RE38492 April 6, 2004 Diab et al.
6721594 April 13, 2004 Conley et al.
6728572 April 27, 2004 Hsu et al.
6748269 June 8, 2004 Thompson et al.
6749566 June 15, 2004 Russ
6751498 June 15, 2004 Greenberg et al.
6760617 July 6, 2004 Ward et al.
6773396 August 10, 2004 Flach et al.
6775566 August 10, 2004 Nissila
6790178 September 14, 2004 Mault et al.
6795722 September 21, 2004 Sheraton et al.
6814706 November 9, 2004 Barton et al.
6816744 November 9, 2004 Garfield et al.
6821249 November 23, 2004 Casscells, III et al.
6824515 November 30, 2004 Suorsa et al.
6827690 December 7, 2004 Bardy
6829503 December 7, 2004 Alt
6858006 February 22, 2005 MacCarter et al.
6871211 March 22, 2005 Labounty et al.
6878121 April 12, 2005 Krausman et al.
6879850 April 12, 2005 Kimball
6881191 April 19, 2005 Oakley et al.
6887201 May 3, 2005 Bardy
6890096 May 10, 2005 Tokita et al.
6893396 May 17, 2005 Schulze et al.
6894204 May 17, 2005 Dunshee
6906530 June 14, 2005 Geisel
6912414 June 28, 2005 Tong
6936006 August 30, 2005 Sabra
6940403 September 6, 2005 Kail, IV
6942622 September 13, 2005 Turcott
6952695 October 4, 2005 Trinks et al.
6970742 November 29, 2005 Mann et al.
6972683 December 6, 2005 Lestienne et al.
6978177 December 20, 2005 Chen et al.
6980851 December 27, 2005 Zhu et al.
6980852 December 27, 2005 Jersey-Willuhn et al.
6985078 January 10, 2006 Suzuki et al.
6987965 January 17, 2006 Ng et al.
6988989 January 24, 2006 Weiner et al.
6993378 January 31, 2006 Wiederhold et al.
6997879 February 14, 2006 Turcott
7003346 February 21, 2006 Singer
7018338 March 28, 2006 Vetter et al.
7020508 March 28, 2006 Stivoric et al.
7027862 April 11, 2006 Dahl et al.
7041062 May 9, 2006 Friedrichs et al.
7044911 May 16, 2006 Drinan et al.
7047067 May 16, 2006 Gray et al.
7050846 May 23, 2006 Sweeney et al.
7054679 May 30, 2006 Hirsh
7059767 June 13, 2006 Tokita et al.
7088242 August 8, 2006 Aupperle et al.
7113826 September 26, 2006 Henry et al.
7118531 October 10, 2006 Krill
7127370 October 24, 2006 Kelly, Jr. et al.
7129836 October 31, 2006 Lawson et al.
7130396 October 31, 2006 Rogers et al.
7130679 October 31, 2006 Parsonnet et al.
7133716 November 7, 2006 Kraemer et al.
7136697 November 14, 2006 Singer
7136703 November 14, 2006 Cappa et al.
7142907 November 28, 2006 Xue et al.
7149574 December 12, 2006 Yun et al.
7149773 December 12, 2006 Haller et al.
7153262 December 26, 2006 Stivoric et al.
7156807 January 2, 2007 Carter et al.
7156808 January 2, 2007 Quy
7160252 January 9, 2007 Cho et al.
7160253 January 9, 2007 Nissila
7166063 January 23, 2007 Rahman et al.
7167743 January 23, 2007 Heruth et al.
7184821 February 27, 2007 Belalcazar et al.
7191000 March 13, 2007 Zhu et al.
7194306 March 20, 2007 Turcott
7206630 April 17, 2007 Tarler
7212849 May 1, 2007 Zhang et al.
7215984 May 8, 2007 Diab et al.
7215991 May 8, 2007 Besson et al.
7238159 July 3, 2007 Banet et al.
7248916 July 24, 2007 Bardy
7251524 July 31, 2007 Hepp et al.
7257438 August 14, 2007 Kinast
7261690 August 28, 2007 Teller et al.
7277741 October 2, 2007 Debreczeny et al.
7284904 October 23, 2007 Tokita et al.
7285090 October 23, 2007 Stivoric et al.
7294105 November 13, 2007 Islam
7295879 November 13, 2007 Denker et al.
7297119 November 20, 2007 Westbrook et al.
7318808 January 15, 2008 Tarassenko et al.
7319386 January 15, 2008 Collins, Jr. et al.
7336187 February 26, 2008 Hubbard, Jr. et al.
7346380 March 18, 2008 Axelgaard et al.
7382247 June 3, 2008 Welch et al.
7384398 June 10, 2008 Gagnadre et al.
7390299 June 24, 2008 Weiner et al.
7395106 July 1, 2008 Ryu et al.
7423526 September 9, 2008 Despotis
7423537 September 9, 2008 Bonnet et al.
7443302 October 28, 2008 Reeder et al.
7450024 November 11, 2008 Wildman et al.
7468032 December 23, 2008 Stahmann et al.
8116841 February 14, 2012 Bly et al.
8249686 August 21, 2012 Libbus et al.
8285356 October 9, 2012 Bly et al.
20010047127 November 29, 2001 New, Jr. et al.
20020019586 February 14, 2002 Teller et al.
20020019588 February 14, 2002 Marro et al.
20020028989 March 7, 2002 Pelletier et al.
20020032581 March 14, 2002 Reitberg
20020045836 April 18, 2002 Alkawwas
20020088465 July 11, 2002 Hill
20020099277 July 25, 2002 Harry et al.
20020116009 August 22, 2002 Fraser et al.
20020123672 September 5, 2002 Christophersom et al.
20020123674 September 5, 2002 Plicchi et al.
20020138017 September 26, 2002 Bui et al.
20020167389 November 14, 2002 Uchikoba et al.
20030009092 January 9, 2003 Parker
20030023184 January 30, 2003 Pitts-Crick et al.
20030028221 February 6, 2003 Zhu et al.
20030028327 February 6, 2003 Brunner et al.
20030051144 March 13, 2003 Williams
20030055460 March 20, 2003 Owen et al.
20030083581 May 1, 2003 Taha et al.
20030085717 May 8, 2003 Cooper
20030087244 May 8, 2003 McCarthy
20030092975 May 15, 2003 Casscells, III et al.
20030093125 May 15, 2003 Zhu et al.
20030093298 May 15, 2003 Hernandez et al.
20030100367 May 29, 2003 Cooke
20030135127 July 17, 2003 Sackner et al.
20030143544 July 31, 2003 McCarthy
20030149349 August 7, 2003 Jensen
20030187370 October 2, 2003 Kodama
20030191503 October 9, 2003 Zhu et al.
20030212319 November 13, 2003 Magill
20030221687 December 4, 2003 Kaigler
20030233129 December 18, 2003 Matos
20040006279 January 8, 2004 Arad
20040010303 January 15, 2004 Bolea et al.
20040015058 January 22, 2004 Besson et al.
20040019292 January 29, 2004 Drinan et al.
20040044293 March 4, 2004 Burton
20040049132 March 11, 2004 Barron et al.
20040073094 April 15, 2004 Baker
20040073126 April 15, 2004 Rowlandson
20040077954 April 22, 2004 Oakley et al.
20040100376 May 27, 2004 Lye et al.
20040102683 May 27, 2004 Khanuja et al.
20040106951 June 3, 2004 Edman et al.
20040127790 July 1, 2004 Lang et al.
20040133079 July 8, 2004 Mazar et al.
20040133081 July 8, 2004 Teller et al.
20040134496 July 15, 2004 Cho et al.
20040143170 July 22, 2004 DuRousseau
20040147969 July 29, 2004 Mann et al.
20040152956 August 5, 2004 Korman
20040158132 August 12, 2004 Zaleski
20040167389 August 26, 2004 Brabrand
20040172080 September 2, 2004 Stadler et al.
20040199056 October 7, 2004 Husemann et al.
20040215240 October 28, 2004 Lovett et al.
20040215247 October 28, 2004 Bolz
20040220639 November 4, 2004 Mulligan et al.
20040225199 November 11, 2004 Evanyk et al.
20040225203 November 11, 2004 Jemison et al.
20040243018 December 2, 2004 Organ et al.
20040267142 December 30, 2004 Paul
20050004506 January 6, 2005 Gyory
20050015094 January 20, 2005 Keller
20050015095 January 20, 2005 Keller
20050020935 January 27, 2005 Helzel et al.
20050027175 February 3, 2005 Yang
20050027204 February 3, 2005 Kligfield et al.
20050027207 February 3, 2005 Westbrook et al.
20050027918 February 3, 2005 Govindarajulu et al.
20050043675 February 24, 2005 Pastore et al.
20050054944 March 10, 2005 Nakada et al.
20050059867 March 17, 2005 Chung
20050065445 March 24, 2005 Arzbaecher et al.
20050065571 March 24, 2005 Imran
20050070768 March 31, 2005 Zhu et al.
20050070778 March 31, 2005 Lackey et al.
20050080346 April 14, 2005 Gianchandani et al.
20050080460 April 14, 2005 Wang et al.
20050080463 April 14, 2005 Stahmann et al.
20050085734 April 21, 2005 Tehrani
20050091338 April 28, 2005 de la Huerga
20050096513 May 5, 2005 Ozguz et al.
20050113703 May 26, 2005 Farringdon et al.
20050124878 June 9, 2005 Sharony
20050124901 June 9, 2005 Misczynski et al.
20050124908 June 9, 2005 Belalcazar et al.
20050131288 June 16, 2005 Turner et al.
20050137464 June 23, 2005 Bomba
20050137626 June 23, 2005 Pastore et al.
20050148895 July 7, 2005 Misczynski et al.
20050158539 July 21, 2005 Murphy et al.
20050177038 August 11, 2005 Kolpin et al.
20050187482 August 25, 2005 O'Brien et al.
20050187796 August 25, 2005 Rosenfeld et al.
20050192488 September 1, 2005 Bryenton et al.
20050197654 September 8, 2005 Edman et al.
20050203433 September 15, 2005 Singer
20050203435 September 15, 2005 Nakada
20050203637 September 15, 2005 Edman et al.
20050206518 September 22, 2005 Welch et al.
20050215914 September 29, 2005 Bornzin et al.
20050215918 September 29, 2005 Frantz et al.
20050228234 October 13, 2005 Yang
20050228238 October 13, 2005 Monitzer
20050228244 October 13, 2005 Banet
20050239493 October 27, 2005 Batkin et al.
20050240087 October 27, 2005 Keenan et al.
20050251044 November 10, 2005 Hoctor et al.
20050256418 November 17, 2005 Mietus et al.
20050261598 November 24, 2005 Banet et al.
20050261743 November 24, 2005 Kroll
20050267376 December 1, 2005 Marossero et al.
20050267377 December 1, 2005 Marossero et al.
20050267381 December 1, 2005 Benditt et al.
20050267541 December 1, 2005 Scheiner et al.
20050273023 December 8, 2005 Bystrom et al.
20050277841 December 15, 2005 Shennib
20050277842 December 15, 2005 Silva
20050277992 December 15, 2005 Koh et al.
20050280531 December 22, 2005 Fadem et al.
20050283197 December 22, 2005 Daum et al.
20050288601 December 29, 2005 Wood et al.
20060004300 January 5, 2006 Kennedy
20060004377 January 5, 2006 Keller
20060009697 January 12, 2006 Banet et al.
20060009701 January 12, 2006 Nissila et al.
20060010090 January 12, 2006 Brockway et al.
20060020218 January 26, 2006 Freeman et al.
20060025661 February 2, 2006 Sweeney et al.
20060030781 February 9, 2006 Shennib
20060030782 February 9, 2006 Shennib
20060030892 February 9, 2006 Kadhiresan et al.
20060031102 February 9, 2006 Teller et al.
20060041280 February 23, 2006 Stahmann et al.
20060047215 March 2, 2006 Newman et al.
20060052678 March 9, 2006 Drinan et al.
20060058543 March 16, 2006 Walter et al.
20060058593 March 16, 2006 Drinan et al.
20060064030 March 23, 2006 Cosentino et al.
20060064040 March 23, 2006 Berger et al.
20060064142 March 23, 2006 Chavan et al.
20060066449 March 30, 2006 Johnson
20060074283 April 6, 2006 Henderson et al.
20060074462 April 6, 2006 Verhoef
20060075257 April 6, 2006 Martis et al.
20060084881 April 20, 2006 Korzinov et al.
20060085049 April 20, 2006 Cory et al.
20060089679 April 27, 2006 Zhu et al.
20060102476 May 18, 2006 Niwa et al.
20060116592 June 1, 2006 Zhou et al.
20060122474 June 8, 2006 Teller et al.
20060135858 June 22, 2006 Nidd et al.
20060142654 June 29, 2006 Rytky
20060142820 June 29, 2006 Von Arx et al.
20060149168 July 6, 2006 Czarnek
20060155183 July 13, 2006 Kroecker et al.
20060155200 July 13, 2006 Ng
20060161073 July 20, 2006 Singer
20060161205 July 20, 2006 Mitrani et al.
20060161459 July 20, 2006 Rosenfeld et al.
20060173257 August 3, 2006 Nagai et al.
20060173269 August 3, 2006 Glossop
20060195020 August 31, 2006 Martin et al.
20060195039 August 31, 2006 Drew et al.
20060195097 August 31, 2006 Evans et al.
20060195144 August 31, 2006 Giftakis et al.
20060202816 September 14, 2006 Crump et al.
20060212097 September 21, 2006 Varadan et al.
20060224051 October 5, 2006 Teller et al.
20060224072 October 5, 2006 Shennib
20060224079 October 5, 2006 Washchuk
20060235281 October 19, 2006 Tuccillo
20060235316 October 19, 2006 Ungless et al.
20060235489 October 19, 2006 Drew et al.
20060238333 October 26, 2006 Welch et al.
20060241641 October 26, 2006 Albans et al.
20060241701 October 26, 2006 Markowitz et al.
20060241722 October 26, 2006 Thacker et al.
20060247545 November 2, 2006 St. Martin
20060252999 November 9, 2006 Devaul et al.
20060253005 November 9, 2006 Drinan et al.
20060253044 November 9, 2006 Zhang et al.
20060258952 November 16, 2006 Stahmann et al.
20060264730 November 23, 2006 Stivoric et al.
20060264767 November 23, 2006 Shennib
20060264776 November 23, 2006 Stahmann et al.
20060271116 November 30, 2006 Stahmann et al.
20060276714 December 7, 2006 Holt et al.
20060281981 December 14, 2006 Jang et al.
20060281996 December 14, 2006 Kuo et al.
20060293571 December 28, 2006 Bao et al.
20060293609 December 28, 2006 Stahmann et al.
20070010721 January 11, 2007 Chen et al.
20070010750 January 11, 2007 Ueno et al.
20070015973 January 18, 2007 Nanikashvili
20070015976 January 18, 2007 Miesel et al.
20070016089 January 18, 2007 Fischell et al.
20070021678 January 25, 2007 Beck et al.
20070021790 January 25, 2007 Kieval et al.
20070021792 January 25, 2007 Kieval et al.
20070021794 January 25, 2007 Kieval et al.
20070021796 January 25, 2007 Kieval et al.
20070021797 January 25, 2007 Kieval et al.
20070021798 January 25, 2007 Kieval et al.
20070021799 January 25, 2007 Kieval et al.
20070027388 February 1, 2007 Chou
20070027497 February 1, 2007 Parnis
20070032749 February 8, 2007 Overall et al.
20070038038 February 15, 2007 Stivoric et al.
20070038078 February 15, 2007 Osadchy
20070038255 February 15, 2007 Kieval et al.
20070038262 February 15, 2007 Kieval et al.
20070043301 February 22, 2007 Martinsen et al.
20070048224 March 1, 2007 Howell et al.
20070060800 March 15, 2007 Drinan et al.
20070060802 March 15, 2007 Ghevondian et al.
20070069887 March 29, 2007 Welch et al.
20070073132 March 29, 2007 Vosch
20070073168 March 29, 2007 Zhang et al.
20070073181 March 29, 2007 Pu et al.
20070073361 March 29, 2007 Goren et al.
20070082189 April 12, 2007 Gillette
20070083092 April 12, 2007 Rippo et al.
20070092862 April 26, 2007 Gerber
20070104840 May 10, 2007 Singer
20070106132 May 10, 2007 Elhag et al.
20070106137 May 10, 2007 Baker, Jr. et al.
20070106167 May 10, 2007 Kinast
20070118039 May 24, 2007 Bodecker et al.
20070123756 May 31, 2007 Kitajima et al.
20070123903 May 31, 2007 Raymond et al.
20070123904 May 31, 2007 Stad et al.
20070129622 June 7, 2007 Bourget et al.
20070129643 June 7, 2007 Kwok et al.
20070129769 June 7, 2007 Bourget et al.
20070142715 June 21, 2007 Banet et al.
20070142732 June 21, 2007 Brockway et al.
20070149282 June 28, 2007 Lu et al.
20070150008 June 28, 2007 Jones et al.
20070150009 June 28, 2007 Kveen et al.
20070150029 June 28, 2007 Bourget et al.
20070162089 July 12, 2007 Mosesov
20070167753 July 19, 2007 Van Wyk et al.
20070167848 July 19, 2007 Kuo et al.
20070167849 July 19, 2007 Zhang et al.
20070167850 July 19, 2007 Russell et al.
20070172424 July 26, 2007 Roser
20070173705 July 26, 2007 Teller et al.
20070180047 August 2, 2007 Dong et al.
20070180140 August 2, 2007 Welch et al.
20070191723 August 16, 2007 Prystowsky et al.
20070207858 September 6, 2007 Breving
20070208233 September 6, 2007 Kovacs
20070208235 September 6, 2007 Besson et al.
20070208262 September 6, 2007 Kovacs
20070208263 September 6, 2007 John et al.
20070232867 October 4, 2007 Hansmann
20070249946 October 25, 2007 Kumar et al.
20070250121 October 25, 2007 Miesel et al.
20070255120 November 1, 2007 Rosnov
20070255153 November 1, 2007 Kumar et al.
20070255184 November 1, 2007 Shennib
20070255531 November 1, 2007 Drew
20070260133 November 8, 2007 Meyer
20070260155 November 8, 2007 Rapoport et al.
20070260289 November 8, 2007 Giftakis et al.
20070273504 November 29, 2007 Tran
20070276273 November 29, 2007 Watson, Jr.
20070282173 December 6, 2007 Wang et al.
20070299325 December 27, 2007 Farrell et al.
20080004499 January 3, 2008 Davis
20080004904 January 3, 2008 Tran
20080021336 January 24, 2008 Dobak
20080024293 January 31, 2008 Stylos
20080024294 January 31, 2008 Mazar
20080033260 February 7, 2008 Sheppard et al.
20080039700 February 14, 2008 Drinan et al.
20080058614 March 6, 2008 Banet et al.
20080058656 March 6, 2008 Costello et al.
20080059239 March 6, 2008 Gerst et al.
20080091089 April 17, 2008 Guillory et al.
20080114220 May 15, 2008 Banet et al.
20080120784 May 29, 2008 Warner et al.
20080139934 June 12, 2008 McMorrow et al.
20080146892 June 19, 2008 LeBoeuf et al.
20080167538 July 10, 2008 Teller et al.
20080171918 July 17, 2008 Teller et al.
20080171922 July 17, 2008 Teller et al.
20080171929 July 17, 2008 Katims
20080183052 July 31, 2008 Teller et al.
20080200774 August 21, 2008 Luo
20080214903 September 4, 2008 Orbach
20080220865 September 11, 2008 Hsu
20080221399 September 11, 2008 Zhou et al.
20080221402 September 11, 2008 Despotis
20080224852 September 18, 2008 Dicks et al.
20080228084 September 18, 2008 Bedard et al.
20080287751 November 20, 2008 Stivoric et al.
20080287752 November 20, 2008 Stroetz et al.
20080293491 November 27, 2008 Wu et al.
20080294019 November 27, 2008 Tran
20080294020 November 27, 2008 Sapounas
20080318681 December 25, 2008 Rofougaran et al.
20080319279 December 25, 2008 Ramsay et al.
20080319282 December 25, 2008 Tran
20080319290 December 25, 2008 Mao et al.
20090005016 January 1, 2009 Eng et al.
20090018410 January 15, 2009 Coene et al.
20090018456 January 15, 2009 Hung
20090048526 February 19, 2009 Aarts
20090054737 February 26, 2009 Magar et al.
20090062670 March 5, 2009 Sterling et al.
20090073991 March 19, 2009 Landrum et al.
20090076336 March 19, 2009 Mazar et al.
20090076340 March 19, 2009 Libbus et al.
20090076341 March 19, 2009 James et al.
20090076342 March 19, 2009 Amurthur et al.
20090076343 March 19, 2009 James et al.
20090076344 March 19, 2009 Libbus et al.
20090076345 March 19, 2009 Manicka et al.
20090076346 March 19, 2009 James et al.
20090076348 March 19, 2009 Manicka et al.
20090076349 March 19, 2009 Libbus et al.
20090076350 March 19, 2009 Bly et al.
20090076363 March 19, 2009 Bly et al.
20090076364 March 19, 2009 Libbus et al.
20090076397 March 19, 2009 Libbus et al.
20090076401 March 19, 2009 Mazar et al.
20090076405 March 19, 2009 Amurthur et al.
20090076410 March 19, 2009 Libbus et al.
20090076559 March 19, 2009 Libbus et al.
20090182204 July 16, 2009 Semler et al.
20090234410 September 17, 2009 Libbus et al.
20090264792 October 22, 2009 Mazar
20090292194 November 26, 2009 Libbus et al.
20100056881 March 4, 2010 Libbus et al.
20100191310 July 29, 2010 Bly et al.
20110144470 June 16, 2011 Mazar et al.
20110245711 October 6, 2011 Katra et al.
Foreign Patent Documents
1579801 September 2005 EP
WO 00/79255 December 2000 WO
WO 01/89362 November 2001 WO
WO 02/092101 November 2002 WO
WO 03/082080 October 2003 WO
WO 2005/051164 June 2005 WO
WO 2005/104930 November 2005 WO
WO 2006/008745 January 2006 WO
WO 2006/102476 September 2006 WO
WO 2006/111878 November 2006 WO
WO 2007/041783 April 2007 WO
WO 2007/106455 September 2007 WO
2009/116906 September 2009 WO
Other references
  • Lauer MS, et al., “Impaired Chronotropic Response to Exercise Stress Testing as a Predictor of Mortality,” JAMA, 1999; 281(6): 524-529.
  • “Acute Decompensated Heart Failure”—Wikipedia Entry, downloaded from: <http://en.wikipedia.org/wiki/Acutedecompensatedheartfailure>, entry page created in 2008, 6 pages total.
  • “Heart Failure”—Wikipedia Entry, downloaded from the Internet: <http://en.wikipedia.org/wiki/Heartfailure>, entry page created in 2003, 17 pages total.
  • 3M Corporation, “3M Surgical Tapes—Choose the Correct Tape” quicksheet (2004).
  • Abraham, “New approaches to monitoring heart failure before symptoms appear,” Rev Cardiovasc Med. 2006 ;7 Suppl 1 :33-41.
  • AD5934: 250 kSPS 12-Bit Impedance Converter Network Analyzer, Analog Devices, Rev. A. Retrieved from the Internet: <<http://www.analog.com/static/imported-files/datasheets/AD5934.pdf>>, 40 pages. Copyright 2005-2008.
  • Adams, Jr. “Guiding heart failure care by invasive hemodynamic measurements: possible or useful?”, Journal of Cardiac Failure 2002; 8(2):71-73.
  • Adamson et al., “Continuous autonomic assessment in patients with symptomatic heart failure: prognostic value of heart rate variability measured by an implanted cardiac resynchronization device ,” Circulation. 2004;110:2389-2394.
  • Adamson et al., “Ongoing right ventricular hemodynamics in heart failure,” J Am Coll Cardiol, 2003; 41:565-57.
  • Adamson, “Integrating device monitoring into the infrastructure and workflow of routine practice,” Rev Cardiovasc Med. 2006 ;7 Suppl 1:42-6.
  • Adhere [presentation], “Insights from the ADHERE Registry: Data from over 100,000 patient cases,” 2005, 70 pages total.
  • Advamed White Sheet, “Health Information Technology: Improving Patient Safety and Quality of Care,” Jun. 2005, 23 pages.
  • Aghababian, “Acutely decompensated heart failure: opportunities to improve care and outcomes in the emergency department,” Rev Cardiovasc Med. 2002;3 Suppl 4:S3-9.
  • Albert, “Bioimpedance to prevent heart failure hospitalization,” Curr Heart Fail Rep. Sep. 2006;3(3):136-42.
  • American Heart Association, “Heart Disease and Stroke Statistics—2006 Update,” 2006, 43 pages.
  • American Heart Association, “Heart Disease and Stroke Statistics—2007 Update. A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee,” Circulation 2007; 115;e69-e171.
  • Belalcazar et al., “Monitoring lung edema using the pacemaker pulse and skin electrodes,” Physiol. Meas. 2005; 26:S153-S163.
  • Bennet, “Development of implantable devices for continuous ambulatory monitoring of central hemodynamic values in heart failure patients,” PACE Jun. 2005; 28:573-584.
  • Bourge, “Case studies in advanced monitoring with the chronicle device,” Rev Cardiovasc Med. 2006 ;7 Suppl 1:S56-61.
  • Braunschweig, “Continous haemodynamic monitoring during withdrawal of diuretics in patients with congestive heart failure,” European Heart Journal 2002 23(1):59-69.
  • Braunschweig, “Dynamic changes in right ventricular pressures during haemodialysis recorded with an implantable haemodynamic monitor ,” Nephrol Dial Transplant 2006; 21:176-183.
  • Brennan, “Measuring a Grounded Impedance Profile Using the AD5933,” Analog Devices, 2006; retrieved from the internet <<http://http://www.analog.com/static/imported-files/applicationnotes/427095282381510189AN8470.pdf>>, 12 pages total.
  • Buono et al., “The effect of ambient air temperature on whole-body bioelectrical impedance,” Physiol. Meas. 2004;25:119-123.
  • Burkhoff et al., “Heart failure with a normal ejection fraction: Is it really a disorder of diastolic function?” Circulation 2003; 107:656-658.
  • Burr et al., “Heart rate variability and 24-hour minimum heart rate,” Biological Research for Nursing, 2006; 7(4):256-267.
  • Cardionet, “CardioNet Mobile Cardiac Outpatient Telemetry: Addendum to Patient Education Guide”, CardioNet, Inc., 2007, 2 pages.
  • Cardionet, “Patient Education Guide”, CardioNet, Inc., 2007, 7 pages.
  • Charach et al., “Transthoracic monitoring of the impedance of the right lung in patients with cardiogenic pulmonary edema,” Crit Care Med Jun. 2001;29(6):1137-1144.
  • Charlson et al., “Can disease management target patients most likely to generate high costs? The Impact of Comorbidity,” Journal of General Internal Medicine, Apr. 2007, 22(4):464-469.
  • Chaudhry et al., “Telemonitoring for patients with chronic heart failure: a systematic review,” J Card Fail. Feb. 2007; 13(1): 56-62.
  • Chung et al., “White coat hypertension: Not so benign after all?,” Journal of Human Hypertension (2003) 17, 807-809.
  • Cleland et al., “The EuroHeart Failure survey programme—a survey on the quality of care among patients with heart failure in Europe—Part 1: patient characteristics and diagnosis,” European Heart Journal 2003 24(5):442-463.
  • Cooley, “The Parameters of Transthoracic Electical Conduction,” Annals of the New York Academy of Sciences, 1970; 170(2):702-713.
  • Cowie et al., “Hospitalization of patients with heart failure. A population-based study,” European Heart Journal 2002 23(11):877-885.
  • Dimri, Chapter 1: Fractals in geophysics and seimology: an introduction, Fractal Behaviour of the Earth System, Springer Berlin Heidelberg 2005, pp. 1-22. [Summary and 1st page Only].
  • El-Dawlatly et al., “Impedance cardiography: noninvasive assessment of hemodynamics and thoracic fluid content during bariatric surgery,” Obesity Surgery, May 2005, 15(5):655-658.
  • EM Microelectronic—Marin SA, “Plastic Flexible LCD,” [product brochure]; retrieved from the Internet: <<http://www.emmicroelectronic.com/Line.asp?IdLine=48>>, copyright 2009, 2 pages total.
  • Erdmann, “Editorials: The value of diuretics in chronic heart failure demonstrated by an implanted haemodynamic monitor,” European Heart Journal 2002 23(1):7-9.
  • FDA—Medtronic Chronicle Implantable Hemodynamic Monitoring System P050032: Panel Package Section 11: Chronicle IHM Summary of Safety and Effectiveness, 2007; retrieved from the Internet: <http://www.fda.gov/OHRMS/DOCKETS/AC/07/briefing/2007-4284b104.pdf>, 77 pages total.
  • FDA—Medtronic Inc., Chronicle 9520B Implantable Hemodynamic Monitor Reference Manual, 2007, 112 pages.
  • FDA Executive Summary Memorandum, prepared for Mar. 1, 2007 meeting of the Circulatory Systems Devices Advisory Panel, P050032 Medtronic, Inc. Chronicle Implantable Hemodynamic Monitor (IHM) System, 23 pages. Retrieved from the Internet: <<http://www.fda.gov/ohrms/dockets/ac/07/briefing/2007-4284b102.pdf>>.
  • FDA Executive Summary, Medtronic Chronicle Implantable Hemodynamic Monitoring System P050032: Panel Package Sponsor Executive Summary; vol. 1, section 4: Executive Summary. 2007, 12 pages total. Retrieved from the Internet: <<http://www.fda.gov/OHRMS/DOCKETS/AA/07/briefing/2007-4284b103.pdf>>.
  • FDA, Draft questions for Chronicle Advisory Panel Meeting, 2007, 3 pages total. Retrieved from the Internet: <<http://www.fda.gov/ohrms/dockets/ac/07/questions/2007-4284q1draft.pdf>>.
  • FDA, References for Mar. 1 Circulatory System Devices Panel, 2007, 1 page total. Retrieved from the Internet: <<http://www.fda.gov/OHRMS/DOCKETS/AC/07/briefing/2007-4284bib101.pdf>>.
  • Fonarow et al., “Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis,” JAMA. Feb. 2, 2005;293(5):572-580.
  • Fonarow, “How well are chronic heart failure patients being managed?”, Rev Cardiovasc Med. 2006;7 Suppl 1:S3-11.
  • Fonarow, “Maximizing Heart Failure Care: Opportunities to Improve Patient Outcomes” [Powerpoint Presentation], A CME National Faculty Program, downloaded from the Internet <<http://www.medreviews.com/media/MaxHFCore.ppt>>, 130 pages total.
  • Fonarow, “Proactive monitoring and management of the chronic heart failure patient,” Rev Cardiovasc Med. 2006; 7 Suppl 1:S1-2.
  • Fonarow, “The Acute Decompensated Heart Failure National Registry (ADHERE): opportunities to improve care of patients hospitalized with acute decompensated heart failure,” Rev Cardiovasc Med. 2003;4 Suppl 7:S21-S30.
  • Ganion et al., “Intrathoracic impedance to monitor heart failure status: a comparison of two methods in a chronic heart failure dog model,” Congest Heart Fail. Jul.-Aug. 2005;11(4):177-81, 211.
  • Gass et al., “Critical pathways in the management of acute decompensated heart failure: A CME-Accredited monograph,” Mount Sinai School of Medicine, 2004, 32 pages total.
  • Gheorghiade et al., “Congestion is an important diagnostic and therapeutic target in heart failure,” Rev Cardiovasc Med. 2006 ;7 Suppl 1 :12-24.
  • Gilliam, III et al., “Changes in heart rate variability, quality of life, and activity in cardiac resynchronization therapy patients: results of the HF-HRV registry,” Pacing and Clinical Electrophysiology, Jan. 18, 2007; 30(1): 56-64.
  • Gilliam, III et al., “Prognostic value of heart rate variability footprint and standard deviation of average 5-minute intrinsic R-R intervals for mortality in cardiac resynchronization therapy patients.,” J Electrocardiol. Oct. 2007;40(4):336-42.
  • Gniadecka, “Localization of dermal edema in lipodermatosclerosis, lymphedema, and cardiac insufficiency high-frequency ultrasound examination of intradermal echogenicity,” J Am Acad oDermatol, Jul. 1996; 35(1):37-41.
  • Goldberg et al., “Randomized trial of a daily electronic home monitoring system in patients with advanced heart failure: The Weight Monitoring in Heart Failure (WHARF) Trial,” American Heart Journal, Oct. 2003; 416(4):705-712.
  • Grap et al., “Actigraphy in the Critically III: Correlation With Activity, Agitation, and Sedation,” American Journal of Critical Care. 2005;14: 52-60.
  • Gudivaka et al., “Single- and multifrequency models for bioelectrical impedance analysis of body water compartments,” J Appl Physiol, 1999;87(3):1087-1096.
  • Guyton et al., Unit V: The Body Fluids and Kidneys, Chapter 25: The Body Fluid Compartments: Extracellular and Intracellular Fluids; Interstitial Fluid and Edema, Guyton & Hall Textbook of Medical Physiology 11th Edition, Saunders 2005; pp. 291-306.
  • Hadase et al., “Very low frequency power of heart rate variability is a powerful predictor of clinical prognosis in patients with congestive heart Failure,” Circ J 2004; 68(4):343-347.
  • Hallstrom et al., “Structural relationships between measures based on heart beat intervals: potential for improved risk assessment,” IEEE Biomedical Engineering 2004, 51(8):1414-1420.
  • HFSA 2006 Comprehensive Heart Failure Practice Guideline—Executive Summary: HFSA 2006 Comprehensive Heart Failure Practice Guideline, Journal of Cardiac Failure 2006;12(1):10-e38.
  • HFSA 2006 Comprehensive Heart Failure Practice Guideline—Section 12: Evaluation and Management of Patients With Acute Decompensated Heart Failure, Journal of Cardiac Failure 2006;12(1):e86-e103.
  • HFSA 2006 Comprehensive Heart Failure Practice Guideline—Section 2: Conceptualization and Working Definition of Heart Failure, Journal of Cardiac Failure 2006;12(1):e10-e11.
  • HFSA 2006 Comprehensive Heart Failure Practice Guideline—Section 3: Prevention of Ventricular Remodeling Cardiac Dysfunction, and Heart Failure Overview, Journal of Cardiac Failure 2006;12(1):e12-e15.
  • HFSA 2006 Comprehensive Heart Failure Practice Guideline—Section 4: Evaluation of Patients for Ventricular Dysfunction and Heart Failure, Journal of Cardiac Failure 2006;12(1):e16-e25.
  • HFSA 2006 Comprehensive Heart Failure Practice Guideline—Section 8: Disease Management in Heart Failure Education and Counseling, Journal of Cardiac Failure 2006;12(1):e58-e68.
  • HRV Enterprises, LLC, “Heart Rate Variability Seminars,” downloaded from the Internet: <<http://hrventerprise.com/>>on Apr. 24, 2008, 3 pages total.
  • HRV Enterprises, LLC, “LoggerPro HRV Biosignal Analysis,” downloaded from the Internet: <<http://hrventerprise.com/products.html on Apr. 24, 2008, 3 pages total.
  • Hunt et al., “ACC/AHA 2005 Guideline Update for the Diagnosis and Management of Chronic Heart Failure in the Adult: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Update the 2001 Guidelines for the Evaluation and Management of Heart Failure): Developed in Collaboration With the American College of Chest Physicians and the International Society for Heart and Lung Transplantation: Endorsed by the Heart Rhythm Society,” Circulation. 2005;112:e154-e235.
  • Hunt et al., ACC/AHA Guidelines for the Evaluation and Management of Chronic Heart Failure in the Adult: Executive Summary A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Revise the 1995 Guidelines for the Evaluation and Management of Heart Failure), Circulation. 2001;104:2996-3007.
  • Imhoff et al., “Noninvasive whole-body electrical bioimpedance cardiac output and invasive thermodilution cardiac output in high-risk surgical patients,” Critical Care Medicine 2000; 28(8):2812-2818.
  • Jaeger et al., “Evidence for Increased Intrathoracic Fluid Volume in Man at High Altitude,” J Appl Physiol 1979; 47(6): 670-676.
  • Jaio et al., “Variance fractal dimension analysis of seismic refraction signals,” WESCANEX 97: Communications, Power and Computing. IEEE Conference Proceedings., May 22-23, 1997, pp. 163-167 [Abstract Only].
  • Jerant et al., “Reducing the cost of frequent hospital admissions for congestive heart failure: a randomized trial of a home telecare intervention,” Medical Care 2001, 39(11):1234-1245.
  • Kasper et al., “A randomized trial of the efficacy of multidisciplinary care in heart failure outpatients at high risk of hospital readmission,” J Am Coll Cardiol, 2002; 39:471-480.
  • Kaukinen, “Cardiac output measurement after coronary artery bypass grafting using bolus thermodilution, continuous thermodilution, and whole-body impedance cardiography,” Journal of Cardiothoracic and Vascular Anesthesia 2003; 17(2):199-203.
  • Kawaguchi et al., “Combined ventricular systolic and arterial stiffening in patients with heart failure and preserved ejection fraction: implications for systolic and diastolic reserve limitations,” Circulation. 2003;107:714-720.
  • Kawasaki et al., “Heart rate turbulence and clinical prognosis in hypertrophic cardiomyopathy and myocardial infarction,” Circ J. Jul. 2003;67(7):601-604.
  • Kearney et al., “Predicting death due to progressive heart failure in patients with mild-to-moderate chronic heart failure,” J Am Coll Cardiol, 2002; 40(10):1801-1808.
  • Kitzman et al., “Pathophysiological characterization of isolated diastolic heart failure in comparison to systolic heart failure,” JAMA Nov. 2002; 288(17):2144-2150.
  • Kööbi et al., “Non-invasive measurement of cardiac output: whole-body impedance cardiography in simultaneous comparison with thermodilution and direct oxygen Fick methods,” Intensive Care Medicine 1997; 23(11):1132-1137.
  • Koyama et al., “Evaluation of heart-rate turbulence as a new prognostic marker in patients with chronic heart failure,” Circ J 2002; 66(10):902-907.
  • Krumholz et al., “Predictors of readmission among elderly survivors of admission with heart failure,” American Heart Journal 2000; 139 (1):72-77.
  • Kyle et al., “Bioelectrical Impedance Analysis—part I: review of principles and methods,” Clin Nutr. Oct. 2004;23(5):1226-1243.
  • Kyle et al., “Bioelectrical Impedance Analysis—part II: utilization in clinical practice,” Clin Nutr. Oct. 2004;23(5):1430-1453.
  • Lee et al., “Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model,” JAMA 2003;290(19):2581-2587.
  • Leier “The Physical Examination in Heart Failure—Part I,” Congest Heart Fail. Jan.-Feb. 2007;13(1):41-47.
  • LifeShirt® Model 200 Directions for Use, “Introduction”, VivoMetrics, Inc. 9 page total.
  • Liu et al., “Fractal analysis with applications to seismological pattern recognition of underground nuclear explosions,” Singal Processing, Sep. 2000, 80(9):1849-1861. [Abstract Only].
  • Lozano-Nieto, “Impedance ratio in bioelectrical impedance measurements for body fluid shift determination,” Proceedings of the IEEE 24th Annual Northeast Bioengineering Conference, Apr. 9-10, 1998, pp. 24-25.
  • Lucreziotti et al., “Five-minute recording of heart rate variability in severe chronic heart failure : Correlates with right ventricular function and prognostic implications,” American Heart Journal 2000; 139(6):1088-1095.
  • Lüthje et al., “Detection of heart failure decompensation using intrathoracic impedance monitoring by a triple-chamber implantable defibrillator,” Heart Rhythm Sep. 2005;2(9):997-999.
  • Magalski et al., “Continuous ambulatory right heart pressure measurements with an implantable hemodynamic monitor: a multicenter, 12-Month Follow-up Study of Patients With Chronic Heart Failure,” J Card Fail 2002, 8(2):63-70.
  • Mahlberg et al., “Actigraphy in agitated patients with dementia: Monitoring treatment outcomes,” Zeitschrift für Gerontologie and Geriatrie, Jun. 2007; 40(3)178-184. [Abstract Only].
  • Matthie et al., “Analytic assessment of the various bioimpedance methods used to estimate body water,” Appl Physiol 1998; 84(5):1801-1816.
  • Matthie, “Second generation mixture theory equation for estimating intracellular water using bioimpedance spectroscopy,” J Appl Physiol 2005; 99:780-781.
  • McMurray et al., “Heart Failure: Epidemiology, Aetiology, and Prognosis of Heart Failure,” Heart 2000;83:596-602.
  • Miller, “Home monitoring for congestive heart failure patients,” Caring Magazine, Aug. 1995: 53-54.
  • Moser et al., “Improving outcomes in heart failure: it's not unusual beyond usual Care,” Circulation. 2002;105:2810-2812.
  • Nagels et al., “Actigraphic measurement of agitated behaviour in dementia,” International journal of geriatric psychiatry , 2009; 21(4):388-393. [Abstract Only].
  • Nakamura et al., “Universal scaling law in human behavioral organization,” Physical Review Letters, Sep. 28, 2007; 99(13):138103 (4 pages).
  • Nakaya, “Fractal properties of seismicity in regions affected by large, shallow earthquakes in western Japan: Implications for fault formation processes based on a binary fractal fracture network model,” Journal of geophysical research, Jan. 2005; 11(B1):B01310.1-B01310.15. [Abstract Only].
  • Naylor et al., “Comprehensive discharge planning for the hospitalized elderly: a randomized clinical trial ,” Amer. College Physicians 1994; 120(12):999-1006.
  • Nesiritide (Natrecor),, [Presentation] Acutely Decompensated Congestive Heart Failure: Burden of Disease, downloaded from the Internet: <<http://www.huntsvillehospital.org/foundation/events/cardiologyupdate/CHF.ppt.>>, 39 pages.
  • Nieminen et al., “EuroHeart Failure Survey II (EHFS II): a survey on hospitalized acute heart failure patients: description of population,” European Heart Journal 2006; 27(22):2725-2736.
  • Nijsen et al., “The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy,” Epilepsy Behay. Aug. 2005;7(1):74-84.
  • Noble et al., “Diuretic induced change in lung water assessed by electrical impedance tomography,” Physiol. Meas. 2000; 21(1):155-163.
  • Noble et al., “Monitoring patients with left ventricular failure by electrical impedance tomography,” Eur J Heart Fail. Dec. 1999;1(4):379-84.
  • O'Connell et al., “Economic impact of heart failure in the United States: time for a different approach,” J Heart Lung Transplant., Jul.-Aug. 1994 ; 13(4):S107-S112.
  • Ohlsson et al., “Central hemodynamic responses during serial exercise tests in heart failure patients using implantable hemodynamic monitors,” Eur J Heart Fail. Jun. 2003;5(3):253-259.
  • Ohlsson et al., “Continuous ambulatory monitoring of absolute right ventricular pressure and mixed venous oxygen saturation in patients with heart failure using an implantable haemodynamic monitor,” European Heart Journal 2001 22(11):942-954.
  • Packer et al., “Utility of impedance cardiography for the identification of short-term risk of clinical decompensation in stable patients with chronic heart failure,” J Am Coll Cardiol, 2006; 47(11):2245-2252.
  • Palatini et al., “Predictive value of clinic and ambulatory heart rate for mortality in elderly subjects with systolic hypertension” Arch Intern Med. 2002;162:2313-2321.
  • Piiria et al., “Crackles in patients with fibrosing alveolitis bronchiectasis, COPD, and Heart Failure,” Chest May 1991; 99(5):1076-1083.
  • Pocock et al., “Predictors of mortality in patients with chronic heart failure,” Eur Heart J 2006; (27): 65-75.
  • Poole-Wilson, “Importance of control of fluid volumes in heart failure,” European Heart Journal 2000; 22(11):893-894.
  • Raj et al., ‘Letter Regarding Article by Adamson et al, “Continuous Autonomic Assessment in Patients With Symptomatic Heart Failure: Prognostic Value of Heart Rate Variability Measured by an Implanted Cardiac Resynchronization Device’”, Circulation 2005;112:e37-e38.
  • Ramirez et al., “Prognostic value of hemodynamic findings from impedance cardiography in hypertensive stroke,” AJH 2005; 18(20):65-72.
  • Rich et al., “A multidisciplinary intervention to prevent the readmission of elderly patients with congestive heart failure,” New Engl. J. Med. 1995;333:1190-1195.
  • Roglieri et al., “Disease management interventions to improve outcomes in congestive heart failure,” Am J Manag Care. Dec. 1997;3(12):1831-1839.
  • Sahalos et al., “The Electrical impedance of the human thorax as a guide in evaluation of intrathoracic fluid volume,” Phys. Med. Biol. 1986; 31:425-439.
  • Saxon et al., “Remote active monitoring in patients with heart failure (rapid-rf): design and rationale,” Journal of Cardiac Failure 2007; 13(4):241-246.
  • Scharf et al., “Direct digital capture of pulse oximetry waveforms,” Proceedings of the Twelfth Southern Biomedical Engineering Conference, 1993., pp. 230-232.
  • Shabetai, “Monitoring heart failure hemodynamics with an implanted device: its potential to improve outcome,” J Am Coll Cardio, 2003; 41:572-573.
  • Small, “Integrating monitoring into the Infrastructure and Workflow of Routine Practice: OptiVol,” Rev Cardiovasc Med. 2006 ;7 Supp 1: S47-S55.
  • Smith et al., “Outcomes in heart failure patients with preserved ejection fraction: mortality, readmission, and functional decline ,” J Am Coll Cardiol, 2003; 41:1510-1518.
  • Something in the way he moves, The Economist, 2007, retrieved from the Internet: <<http://www.economist.com/science/printerFriendly.cfm?story id=9861412>>.
  • Starling, “Improving care of chronic heart failure: advances from drugs to devices,” Cleveland Clinic Journal of Medicine Feb. 2003; 70(2):141-146.
  • Steijaert et al., “The use of multi-frequency impedance to determine total body water and extracellular water in obese and lean female individuals,” International Journal of Obesity Oct. 1997; 21(10):930-934.
  • Stewart et al., “Effects of a home-based intervention among patients with congestive heart failure discharged from acute hospital care,” Arch Intern Med. 1998;158:1067-1072.
  • Stewart et al., “Effects of a multidisciplinary, home-based intervention on planned readmissions and survival among patients with chronic congestive heart failure: a randomised controlled study,” The Lancet Sep. 1999, 354(9184):1077-1083.
  • Stewart et al., “Home-based intervention in congestive heart failure: long-term implications on readmission and survival,” Circulation. 2002;105:2861-2866.
  • Stewart et al., “Prolonged beneficial effects of a home-based intervention on unplanned readmissions and mortality among patients with congestive heart failure,” Arch Intern Med. 1999;159:257-261.
  • Stewart et al., “Trends in Hospitalization for Heart Failure in Scotland, 1990-1996. An Epidemic that has Reached Its Peak?,” European Heart Journal 2001 22(3):209-217.
  • Swedberg et al., “Guidelines for the diagnosis and treatment of chronic heart failure: executive summary (update 2005): The Task Force for the Diagnosis and Treatment of Chronic Heart Failure of the European Society of Cardiology,” Eur Heart J. Jun. 2005; 26(11):1115-1140.
  • Tang, “Case studies in advanced monitoring: OptiVol,” Rev Cardiovasc Med. 2006;7 Suppl 1:S62-S66.
  • The ESCAPE Investigators and ESCAPE Study Coordinators, “Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness,” JAMA 2005;294:1625-1633.
  • Tosi et al., “Seismic signal detection by fractal dimension analysis ,” Bulletin of the Seismological Society of America; Aug. 1999; 89(4):970-977. [Abstract Only].
  • Van De Water et al., “Monitoring the chest with impedance,” Chest. 1973;64:597-603.
  • Van Someren, “Actigraphic monitoring of movement and rest-activity rhythms inaging, Alzheimer's disease, and Parkinson's disease,” IEEE Transactions on Rehabilitation Engineering, Dec. 1997; 5(4):394-398. [Abstract Only].
  • Vasan et al., “Congestive heart failure in subjects with normal versus reduced left ventricular ejection fraction,” J Am Coll Cardiol, 1999; 33:1948-1955.
  • Verdecchia et al., “Adverse prognostic value of a blunted circadian rhythm of heart rate in essential hypertension,” Journal of Hypertension 1998; 16(9):1335-1343.
  • Verdecchia et al., “Ambulatory pulse pressure: a potent predictor of total cardiovascular risk in hypertension,” Hypertension. 1998;32:983-988.
  • Vollmann et al., “Clinical utility of intrathoracic impedance monitoring to alert patients with an implanted device of deteriorating chronic heart failure,” Euorpean Heart Journal Advance Access published on Feb. 19, 2007, downloaded from the Internet:<<http://eurheartj.oxfordjournals.org/cgi/content/full/ehl506v1>>, 6 pages total.
  • Vuksanovic et al., “Effect of posture on heart rate variability spectral measures in children and young adults with heart disease,” International Journal of Cardiology 2005;101(2): 273-278.
  • Wang et al., “Feasibility of using an implantable system to measure thoracic congestion in an ambulatory chronic heart failure canine model,” PACE 2005;28(5):404-411.
  • Wickemeyer et al., #197 —“Association between atrial and ventricular tachyarrhythmias, intrathoracic impedance and heart failure decompensation in CRT-D Patients,” Journal of Cardiac Failure 2007; 13 (6) Suppl.; S131-132.
  • Williams et al, “How do different indicators of cardiac pump function impact upon the long-term prognosis of patients with chronic heart failure,” American Heart Journal, 150(5 ):983.e1-983.e6.
  • Wonisch et al., “Continuous haemodynamic monitoring during exercise in patients with pulmonary hypertension,” Int J Cardiol. Jun. 8, 2005;101(3):415-420.
  • Wynne et al., “Impedance cardiography: a potential monitor for hemodialysis,” Journal of Surgical Research 2006, 133(1):55-60.
  • Yancy “Current approaches to monitoring and management of heart failure,” Rev Cardiovasc Med 2006; 7 Suppl 1:S25-32.
  • Ypenburg et al., “Intrathoracic Impedance Monitoring to Predict Decompensated Heart Failure,” Am J Cardiol 2007, 99(4):554-557.
  • Yu et al., “Intrathoracic Impedance Monitoring in Patients With Heart Failure: Correlation With Fluid Status and Feasibility of Early Warning Preceding Hospitalization,” Circulation. 2005;112:841-848.
  • Zannad et al., “Incidence, clinical and etiologic features, and outcomes of advanced chronic heart failure: The EPICAL Study,” J Am Coll Cardiol, 1999; 33(3):734-742.
  • Zile, “Heart failure with preserved ejection fraction: is this diastolic heart failure?” J Am Coll Cardiol, 2003; 41(9):1519-1522.
  • U.S. Appl. No. 60/006,600, filed Nov. 13, 1995; inventor: Terry E. Flach.
  • U.S. Appl. No. 60/972,316, filed Sep. 12, 2008; inventor: Imad Libbus et al.
  • U.S. Appl. No. 60/972,329, filed Sep. 14, 2007; inventor: Yatheendhar Manicka et al.
  • U.S. Appl. No. 60/972,333, filed Sep. 14, 2007; inventor: Mark Bly et al.
  • U.S. Appl. No. 60/972,336, filed Sep. 14, 2007; inventor: James Kristofer et al.
  • U.S. Appl. No. 60/972,340, filed Sep. 14, 2007; inventor: James Kristofer et al.
  • U.S. Appl. No. 60/972,343, filed Sep. 14, 2007; inventor: James Kristofer et al.
  • U.S. Appl. No. 60/972,354, filed Sep. 14, 2007; inventor: Scott Thomas Mazar et al.
  • U.S. Appl. No. 60/972,359, filed Sep. 14, 2007; inventor: Badri Amurthur et al.
  • U.S. Appl. No. 60/972,363, filed Sep. 14, 2007; inventor: Badri Amurthur et al.
  • U.S. Appl. No. 60/972,512, filed Sep. 14, 2007; inventor: Imad Libbus et al.
  • U.S. Appl. No. 60/972,537 filed Sep. 14, 2007; inventor: Yatheendhar Manicka et al.
  • U.S. Appl. No. 60/972,581, filed Sep. 14, 2007; inventor: Imad Libbus et al.
  • U.S. Appl. No. 60/972,616, filed Sep. 14, 2007; inventor: Imad Libbus et al.
  • U.S. Appl. No. 60/972,629, filed Sep. 14, 2007; inventor: Mark Bly et al.
  • U.S. Appl. No. 61/035,970, filed Mar. 12, 2008; inventor: Imad Libbus et al.
  • U.S. Appl. No. 61/046,196 filed Apr. 18, 2008; inventor: Scott T. Mazar.
  • U.S. Appl. No. 61/047,875, filed Apr. 25, 2008; inventor: Imad Libbus et al.
  • U.S. Appl. No. 61/055,645, filed May 23, 2008; inventor: Mark Bly et al.
  • U.S. Appl. No. 61/055,656, filed May 23, 2008; inventor: Imad Libbus et al.
  • U.S. Appl. No. 61/055,666, filed May 23, 2008; inventor: Yatheendhar Manicka et al.
  • U.S. Appl. No. 61/084,567, filed Jul. 29, 2008; inventor: Mark Bly.
Patent History
Patent number: 8790259
Type: Grant
Filed: Oct 22, 2010
Date of Patent: Jul 29, 2014
Patent Publication Number: 20110270049
Assignee: Corventis, Inc. (San Jose, CA)
Inventors: Rodolphe Katra (Blaine, MN), Niranjan Chakravarthy (Minneapolis, MN), Imad Libbus (Saint Paul, MN)
Primary Examiner: Michael Kahelin
Assistant Examiner: Mitchell E Alter
Application Number: 12/910,076
Classifications
Current U.S. Class: Via Monitoring A Plurality Of Physiological Data, E.g., Pulse And Blood Pressure (600/301); Heart (600/508)
International Classification: A61B 5/00 (20060101); A61B 5/02 (20060101); A61B 5/053 (20060101); A61B 5/11 (20060101); A61B 5/0245 (20060101); A61B 5/0205 (20060101); A61B 5/08 (20060101);